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Commit 4cd64b2f authored by Duchateau Fabien's avatar Duchateau Fabien
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[MD] deleted all files + updated README with links to gitlab.com

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README.md
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......@@ -13,9 +13,6 @@ This database contains 50,000 neighbourhoods and 550 indicators (in average) per
It is hosted on the project [mongiris](https://gitlab.liris.cnrs.fr/fduchate/mongiris).
Predihood is now at [https://gitlab.com/fduchate/predihood](https://gitlab.com/fduchate/predihood).
- Python, version >=3.8
- [MongoDB](https://www.mongodb.com/), version >=4 for importing the database about neighbourhoods.
- We recommend to run Predihood on Firefox or Chrome (Safari get some issues)
## Version française
......@@ -26,45 +23,3 @@ Cette base est hébergé dans le projet [mongiris](https://gitlab.liris.cnrs.fr/
Ce dépôt ne permettant pas de collaboration externe, la dernière version de Predihood a été déplacée sur [https://gitlab.com/fduchate/predihood](https://gitlab.com/fduchate/predihood).
This command install dependencies, including [mongiris](https://gitlab.liris.cnrs.fr/fduchate/mongiris), a lightweight API which enables the querying of the MongoDB database containing information about French neighbourhoods. Note that the download time may be quite long, as the mongiris API includes a dump of French neighbourhoods (700 MB).
Next, to install the database, run the MongoDB server and execute this command (from the MongoDB's executables directory if needed):
```
./mongorestore --archive=/path/to/dump-dbinsee.bin
```
where `/path/to/` is the path to the dump file of the IRIS collection (provided with the package mongiris in `mongiris/data/dump-iris.bin`).
You may have to create two folders for Mongodb: `data/db` under `PATH/TO/MONGODB/bin`. A tip is to move the dump into that folder and do the following steps:
1. In a first terminal: `./mongod --dbpath=./data/db` (to run mongodb),
2. In a second terminal: `./mongorestore --archive=./data/db/dump-dbinsee.bin` (to restore data from the dump).
### Run Predihood
For running *Predihood*, go in the `predihood/predihood/` directory (which contains `main.py`) and run in a terminal:
```
python3 main.py
```
After some logging information, the terminal displays the URL for testing *Predihood* : `http://localhost:8081/`.
## Example usage
For the cartographic interface, an example would be:
1. Type a query in the panel on the left, e.g. "Lyon". This will display all neighbourhoods that contain "Lyon" in their name or their township.
2. Click on a neighbourhood (which are the small areas in blue). A tooltip will appear with some information about the neighbourhood. There are more informations when clicking on the "More details" link.
3. In order to predict the environment variables, you have to choose the classifier. The "Random Forest" classifier is recommended by default. After some seconds, predictions will appear in the tooltip. This will help you for comparing neighbourhoods between them.s
For the algorithmic interface, an example would be:
1. Choose an algorithm
2. Tune it as desired
3. Click on "Train, test and evaluate" button. When computing accuracies is done, a table shows results for each environment variable and each list of indicators.
## Tests
Tests are in `tests.py` file.
predihood/__init__.py deleted 100644 → 0
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import logging
logging.basicConfig(level=logging.DEBUG)
predihood/classes/Data.py deleted 100644 → 0
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import logging
import sys
import numpy as np
import os
import pandas as pd
import warnings
from area import area
from predihood import model
from predihood.cleaning import clean
from predihood.config import FILE_CLEANED_DATA, FOLDER_DATASETS, ENVIRONMENT_VARIABLES, FILE_GROUPING, NEW_PREFIX, \
OLD_PREFIX, FILE_MANUAL_ASSESSMENT, FILE_MANUAL_ASSESSMENT_EXPERTS
from predihood.utility_functions import address_to_code, append_indicator, append_target
log = logging.getLogger(__name__)
warnings.filterwarnings("ignore", category=RuntimeWarning)
class Data:
def __init__(self, normalization="density", filtering=True, add_assessment=False):
"""
Constructor of the Data class. Initialize attributes.
Args:
normalization: A string to indicate the choice for normalization ("density" for density, "population" for population and None to do not normalize)
filtering: True or False to indicate if useless indicators will be removed or not
"""
self.old_path = FILE_CLEANED_DATA
self.dataset_path = os.path.join(FOLDER_DATASETS, "data_"+str(normalization)+".csv")
self.filtered_path = os.path.join(FOLDER_DATASETS, "data_"+str(normalization)+"_filtered.csv")
self.assessment_path = os.path.join(FOLDER_DATASETS, "data_" + str(normalization) + "with_assessment.csv")
self.assessment_filtered_path = os.path.join(FOLDER_DATASETS, "data_" + str(normalization) + "_filtered_with_assessment.csv")
self.data = None
self.indicators = None
self.normalization = normalization
self.filtering = filtering
self.add_assessment = add_assessment
# retrieve indicators
self.get_indicators()
log.debug("Starting with %d indicators", len(self.indicators))
# define indicators that are not relevant for the prediction and remove them
indicators_to_remove = ["IRIS", "REG", "DEP", "UU2010", "COM", "LIBCOM", "TRIRIS", "GRD_QUART", "LIBIRIS", "TYP_IRIS", "MODIF_IRIS", "LAB_IRIS", "LIB_IRIS", "LIB_COM", "CODGEO", "LIBGEO"]
for indicator in indicators_to_remove:
if indicator in self.indicators:
self.indicators.remove(indicator)
log.debug("Descriptive indicators: %d. It remains %d indicators", len(indicators_to_remove), len(self.indicators))
def get_indicators(self):
"""
Get indicators from the dataset if it exists, else from the database.
"""
if self.data is not None:
self.indicators = self.data.columns.tolist()
# self.data.columns gets all columns, so to get indicators, we remove "CODE" (since it is not relevant for prediction).
# We keep "AREA" and "DENSITY" since they are use as features in prediction
if "CODE" in self.indicators: self.indicators.remove("CODE")
for env in ENVIRONMENT_VARIABLES:
if env in self.indicators:
self.indicators.remove(env)
else:
self.indicators = model.get_indicators_list()
def init_all_in_one(self):
"""
Create or read the dataset. It applies normalization and/or filtering if needed.
"""
if self.add_assessment:
if not os.path.exists(self.assessment_path):
if os.path.exists(self.dataset_path):
self.data = pd.read_csv(self.dataset_path) # data is already created so just read it
else:
self.create() # data does not exist, need to create it
self.add_manually_assessed_iris() # complete data with manually assessed IRIS
self.apply_normalization()
if self.filtering: self.apply_filtering()
elif not os.path.exists(self.assessment_filtered_path):
if self.filtering: self.apply_filtering()
else:
self.read()
else:
if not os.path.exists(self.dataset_path):
self.create()
self.apply_normalization()
if self.filtering: self.apply_filtering()
elif not os.path.exists(self.filtered_path):
if self.filtering: self.apply_filtering()
else:
self.read()
print(len(self.data))
def read(self):
"""
Read the dataset stored in the CSV file (and get indicators from this dataset).
"""
if self.add_assessment:
if self.filtering:
self.data = pd.read_csv(self.assessment_filtered_path)
else:
self.data = pd.read_csv(self.assessment_path)
else:
if self.filtering:
self.data = pd.read_csv(self.filtered_path)
else:
self.data = pd.read_csv(self.dataset_path)
self.get_indicators()
def create(self):
"""
Construct the dataset from HomeInLove data, i.e. ungroup addresses, retrieve information about IRIS and construct a CSV file.
"""
log.info("... construction of dataset is in progress ...")
# 1. read data to transform
if not os.path.exists(self.old_path): clean()
data = pd.read_csv(self.old_path)
# 2. define some variables
raw_data = []
append_col = True
null_iris = 0 # count IRIS that can't be retrieve from its code
foreigners = 0 # count addresses that are not located in France
problems = 0 # count IRIS that encounter problems and that will be not added to the dataset
cols = []
columns = ["address", "country"]
columns.extend(ENVIRONMENT_VARIABLES)
cols_departure = [OLD_PREFIX+env for env in columns]
cols_arrival = [NEW_PREFIX+env for env in columns]
# 3. get data for departure and arrival IRIS
departures = data[cols_departure]
arrivals = data[cols_arrival]
# remove "old_address", "old_country", "new_address", "new_country"
cols_departure.pop(0)
cols_departure.pop(0)
cols_arrival.pop(0)
cols_arrival.pop(0)
cols.append("CODE")
cols.append("AREA")
cols.append("DENSITY")
# 4. for each departure and arrival IRIS (one line in cleanedData.csv), get its INSEE indicators and its EV.
# for each IRIS store its code, compute and store its area and its density, store all its INSEE indicators and its 6 EV.
for (index1, row1), (index2, row2) in zip(departures.iterrows(), arrivals.iterrows()):
dep_row, arr_row = [], []
# convert address to IRIS code and append it to data only if the address is in France
if row1[OLD_PREFIX + "country"] == "France":
dep_code = address_to_code(row1[OLD_PREFIX + "address"])
dep_row.append(dep_code)
dep_iris = model.get_iris_from_code(dep_code) # get IRIS information
coords_dep = model.get_coords_from_code(dep_code)
area_dep = area(coords_dep) / 1000000 if coords_dep is not None else None # convert area from m^2 to km^2
dep_row.append(area_dep) # add area as a feature
density = dep_iris["properties"]["raw_indicators"]["P14_POP"] / area_dep if area_dep is not None and area_dep > 0 else None
dep_row.append(density) # add density as a feature
if dep_iris is not None:
# a. append INSEE indicators
for raw_indicator in self.indicators:
dep_row, cols = append_indicator(raw_indicator, dep_iris, dep_row, append_col, cols)
# b. append EV
for target in cols_departure:
dep_row = append_target(row1, target, dep_row)
append_col = False
if len(dep_row) > 0: raw_data.append(dep_row)
else: problems += 1
else: null_iris += 1
elif row1[OLD_PREFIX+"country"] == "" or row1[OLD_PREFIX+"country"] is None or row1[OLD_PREFIX+"country"] is np.nan: null_iris += 1
else: foreigners += 1
if row2[NEW_PREFIX + "country"] == "France":
arr_code = address_to_code(row2[NEW_PREFIX + "address"])
arr_row.append(arr_code)
arr_iris = model.get_iris_from_code(arr_code)
coords_arr = model.get_coords_from_code(arr_code)
area_arr = area(coords_arr) / 1000000 if coords_arr is not None else None # convert area from m^2 to km^2
arr_row.append(area_arr) # add area as a feature
density = arr_iris["properties"]["raw_indicators"]["P14_POP"] / area_arr if area_arr is not None and area_arr > 0 else None
arr_row.append(density) # add density as a feature
if arr_iris is not None:
# a. append INSEE indicators
for raw_indicator in self.indicators:
arr_row, cols = append_indicator(raw_indicator, arr_iris, arr_row, append_col, cols)
# b. append targets
for target in cols_arrival:
arr_row = append_target(row2, target, arr_row)
append_col = False
if len(arr_row) > 0: raw_data.append(arr_row)
else: problems += 1
else: null_iris += 1
elif row2[NEW_PREFIX + "country"] == "" or row2[NEW_PREFIX + "country"] is None or row2[NEW_PREFIX + "country"] is np.nan: null_iris += 1
else: foreigners += 1
sys.stdout.write("\r%.2f%%" % ((index1 * 100) / len(departures))) # update progress percentage
sys.stdout.flush()
print()
cols.extend(ENVIRONMENT_VARIABLES)
log.info("%d addresses are not located in France.", foreigners)
log.info("%d null IRIS have been removed from the dataset.", null_iris)
log.info("%d IRIS have encountered problems.", problems)
# 5. convert IRIS data into a DataFrame, fill missing values and remove fully empty columns.
self.data = pd.DataFrame(raw_data, columns=cols)
self.data.sort_values("CODE", inplace=True)
self.fill_missing_values("median")
nan_columns = self.data.columns[self.data.isna().all()].tolist()
log.debug("There are %d NaN columns", len(nan_columns))
self.data.drop(nan_columns, axis=1, inplace=True)
self.indicators = [indicator for indicator in self.indicators if indicator not in nan_columns] # remove names of NaN columns in the list of indicators
def fill_missing_values(self, method="median"):
"""
Fill NaN values given the method.
Args:
method: A string corresponding tto the method for filling NaN values ("zero", "mean" or "median"). Default is median.
"""
assert method in ["zero", "mean", "median"]
cols = ["CODE", "AREA", "DENSITY"]
cols.extend(ENVIRONMENT_VARIABLES)
for col in self.data.iteritems():
value = 0
col_name = col[0]
if col_name not in cols:
# fill missing INSEE indicators
if method == "zero":
value = 0
elif method == "mean":
value = self.data[col_name].mean()
elif method == "median":
value = self.data[col_name].median()
else:
value = np.mean(self.data[col_name])
elif col_name not in ["CODE", "AREA", "DENSITY"]:
# fill missing EV
if method == "zero":
env_values = {"building_type": "Housing estates",
"building_usage": "Other activities",
"landscape": "Urban",
"morphological_position": "Central",
"geographical_position": "Centre",
"social_class": "Lower"}
else: # method == "mean" or method == "median"
env_values = {"building_type": "Mixed",
"building_usage": "Other activities",
"landscape": "Green areas",
"morphological_position": "Urban",
"geographical_position": "Centre",
"social_class": "Middle"}
value = env_values[col_name]
# else: -> column is CODE or AREA or DENSITY -> do nothing
self.data[col_name].replace([np.nan], [value], inplace=True)
def filter_too_detailed_indicators(self):
"""
Remove too detailed indicators from the dataset. The list of indicators is given by the file `regrouping.csv`.
"""
regrouping = pd.read_csv(FILE_GROUPING, sep="\t")
status = regrouping.index[regrouping["STATUS"] == 1].tolist() # indicators that are an element of a subset
col_names = [regrouping.iloc[status]["INDICATOR"] for status in status]
# self.filtered_data = self.data[:] # copy by value, not by reference
counter1, counter2 = 0, 0
for column in col_names:
if column in self.data and column in self.indicators and column:
del self.data[column]
self.indicators.remove(column)
counter1 += 1
log.debug("%d indicators have been using regrouping.", counter1)
def apply_filtering(self):
"""
Remove (filter) too detailed indicators and constant columns from the dataset.
"""
if self.data is None:
if self.add_assessment:
self.data = pd.read_csv(self.assessment_path)
else:
self.data = pd.read_csv(self.dataset_path)
self.get_indicators()
# 1. remove indicators that have been defined as useless
self.filter_too_detailed_indicators()
# 2. remove constant columns, i.e. with a null variance
constant_columns = self.data.columns[self.data.nunique() == 1].tolist()
self.data.drop(constant_columns, axis=1, inplace=True)
self.indicators = [indicator for indicator in self.indicators if indicator not in constant_columns]
if self.add_assessment: self.data.to_csv(self.assessment_filtered_path, index=None)
else: self.data.to_csv(self.filtered_path, index=None) # index=None: avoid line numbering
def apply_normalization(self):
"""
Normalize the dataset with the given method, i.e. None, "population" or "density". Default is "density".
"""
assert self.normalization in [None, "population", "density"]
do_not_normalize = ["CODE", "AREA", "DENSITY", "P14_POP"]
do_not_normalize.extend(ENVIRONMENT_VARIABLES) # extend does not return a list
for index, row in self.data.iterrows():
for column in row.iteritems():
col_name = column[0]
if col_name not in do_not_normalize:
if self.normalization == "population":
self.data.at[index, col_name] = row[col_name] / row["P14_POP"]
elif self.normalization == "density":
density = row[self.data.columns.get_loc("DENSITY")]
self.data.at[index, col_name] = row[col_name] / density if density > 0 else 0
elif self.normalization is None:
self.data.at[index, col_name] = row[col_name]
if self.add_assessment: self.data.to_csv(self.assessment_path, index=None)
else: self.data.to_csv(self.dataset_path, index=None) # index=None: avoid line numbering
def add_manually_assessed_iris(self):
manually_assessed_iris = pd.read_csv(FILE_MANUAL_ASSESSMENT_EXPERTS, header=0)
self.data = self.data.append(manually_assessed_iris)
self.data["CODE"] = self.data["CODE"].astype(str)
self.data.sort_values("CODE", inplace=True)
self.fill_missing_values("median")
nan_columns = self.data.columns[self.data.isna().all()].tolist()
log.debug("There are %d NaN columns", len(nan_columns))
self.data.drop(nan_columns, axis=1, inplace=True)
self.indicators = [indicator for indicator in self.indicators if indicator not in nan_columns] # remove names of NaN columns in the list of indicators
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import logging
import pandas as pd
from predihood.config import TRAIN_SIZE, TEST_SIZE, ENVIRONMENT_VARIABLES, RANDOM_STATE, FILE_ENV
from predihood.utility_functions import check_train_test_percentages
from sklearn.ensemble import IsolationForest
from sklearn.model_selection import train_test_split
log = logging.getLogger(__name__)
class Dataset:
"""
This class represents assessed IRIS with their indicators ans EV values. There are options, such as removing outliers or rural IRIS.
"""
def __init__(self, data, env, _type, selected_indicators=None, indicators_to_remove=None, train_size=TRAIN_SIZE, test_size=TEST_SIZE, outliers=False, remove_rural=False):
"""
Constructor of the Dataset class. Initialize attributes.
Args:
data: an instance of Data class. Don"t forget to initialize data after created it with "init_all_in_one()" method
env: a string representing the EV, i.e. a value in ["building_type", "building_usage", "landscape", "morphological_position", "geographical_position", "social_class"]
selected_indicators: a list containing the indicators to keep in the dataset
indicators_to_remove: a list containing the indicators to remove in the dataset
train_size: a integer or a float corresponding to the size of the dataset used for training
test_size: a integer or a float corresponding to the size of the dataset used for test
outliers: True or False to remove outliers from dataset (detected with IsolationForest algorithm)
"""
self.data = data.data[:] # data must be a Data object
self.indicators = data.indicators[:]
self.filtering = data.filtering
self.normalization = data.normalization
self.selected_indicators = selected_indicators[:] if selected_indicators is not None else None
self.indicators_to_remove = indicators_to_remove[:] if indicators_to_remove is not None else None
self.type = _type
self.X = None
self.Y = None
self.X_train = None
self.Y_train = None
self.X_test = None
self.Y_test = None
if env in ENVIRONMENT_VARIABLES: self.env = env
else: self.env = "building_type"
self.train_size, self.test_size, _ = check_train_test_percentages(train_size, test_size)
self.outliers = outliers
self.remove_rural = remove_rural
def init_all_in_one(self):
"""
Initialize the dataset by initializing X and Y ; generating X_train, Y_train, X_test, Y_test ; removing outliers if needed.
When the type is "unsupervised", split data into X and Y is not relevant (as there is no train/test sets).
"""
if self.type == "supervised":
if self.remove_rural: self.remove_rural_iris()
self.init_X()
self.init_Y()
self.train_test()
if self.outliers:
self.remove_outliers() # after train_test() because Isolation Forest needs X_train and X_test
self.init_X()
self.init_Y()
self.train_test() # need to compute train_test to update X and Y after dropping outliers
def init_X(self):
"""
Initialize self.X by getting indicators in dataset.
"""
assert self.data is not None
if self.selected_indicators is not None: # select given indicators
self.X = self.data.loc[:, self.selected_indicators]
if self.indicators_to_remove: # remove given indicators
for indicator in self.indicators_to_remove:
if indicator in self.selected_indicators and indicator in self.X.columns:
self.selected_indicators.remove(indicator)
self.X = self.X.drop([indicator], axis=1)
else:
self.X = self.data.loc[:, self.indicators]
if self.indicators_to_remove: # remove given indicators
for indicator in self.indicators_to_remove:
if indicator in self.indicators and indicator in self.X.columns:
self.indicators.remove(indicator)
self.X = self.X.drop([indicator], axis=1)
def init_Y(self):
"""
Initialize self.Y by getting EV in dataset.
"""
assert self.data is not None
self.Y = self.data[self.env].values
def train_test(self):
"""
Create X_train, Y_train, X_test, Y_test with train_test_split method
"""
if len(self.X) <= 0: self.init_X()
if len(self.Y) <= 0: self.init_Y()
self.X_train, self.X_test, self.Y_train, self.Y_test = train_test_split(self.X, self.Y, train_size=self.train_size, test_size=self.test_size, random_state=RANDOM_STATE) # , stratify=self.Y)
def remove_outliers(self):
"""
Detect and remove IRIS that are outliers from dataset.
"""
isolation_forest = IsolationForest(random_state=RANDOM_STATE, n_estimators=100) # IsolationForest is used to detect outliers
isolation_forest.fit(self.X_train)
predictions = isolation_forest.predict(self.X_test)
for i in range(len(predictions)):
if predictions[i] == -1: # this IRIS is an outlier according to isolation forest algorithm
# delete IRIS that are detected as outliers, ignore id the iris is not present in the dataset (e.g. because it has been remove as a rural IRIS)
self.data = self.data.drop(i, axis=0, errors="ignore")
def remove_rural_iris(self):
"""
Remove from dataset IRIS that are assessed as rural (because they bias the prediction).
"""
self.data = self.data[self.data.morphological_position != "Rural"]
def get_environment_variable(self):
"""
Get values for a given EV for each assessed IRIS and store it in a CSV file.
"""
assert self.env in ENVIRONMENT_VARIABLES
data_env = pd.DataFrame(self.data[['CODE', self.env]])
data_env.to_csv(FILE_ENV)
def get_all_environmental_variable(self):
"""
Get EV for each assessed IRIS and store it in a CSV file.
"""
columns = ['CODE']
columns.extend(ENVIRONMENT_VARIABLES)
data_env = pd.DataFrame(self.data[columns])
data_env.to_csv(FILE_ENV)
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# define available classifiers for the algorithmic interface
import os
import re
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import importlib
tree = os.listdir('algorithms')
regex = re.compile(r'__[^_]*__')
tree_filtered = [i for i in tree if not regex.match(i)] # remove undesirable files such as __pycache__
AVAILABLE_CLASSIFIERS = {
"Random Forest Classifier": RandomForestClassifier,
"KNeighbors Classifier": KNeighborsClassifier,
"Decision Tree Classifier": DecisionTreeClassifier,
"SVC": SVC,
"AdaBoost Classifier": AdaBoostClassifier,
"MLP Classifier": MLPClassifier
}
for i in tree_filtered:
name = i[:-3] # remove .py extension to get only the name of the class
module = importlib.import_module("predihood.algorithms."+name)
class_ = getattr(module, name) # create an object representing the algorithm
AVAILABLE_CLASSIFIERS[name] = class_
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import os
# 1. define files used by Predihood (reading and writing files)
ROOT_FOLDER = os.path.join("..", "predihood")
FOLDER_DATA = os.path.join(ROOT_FOLDER, "generated_files")
FOLDER_CLASSES = os.path.join(ROOT_FOLDER, "classes")
if not os.path.exists(FOLDER_DATA): os.mkdir(FOLDER_DATA)
FOLDER_DATASETS = os.path.join(FOLDER_DATA, "datasets")
if not os.path.exists(FOLDER_DATASETS): os.mkdir(FOLDER_DATASETS)
FOLDER_DISTRIBUTION = os.path.join(FOLDER_DATA, "distribution-plots")
if not os.path.exists(FOLDER_DISTRIBUTION): os.mkdir(FOLDER_DISTRIBUTION)
FOLDER_SELECTED_INDICATORS = os.path.join(FOLDER_DATA, "selected-indicators")
if not os.path.exists(FOLDER_SELECTED_INDICATORS): os.mkdir(FOLDER_SELECTED_INDICATORS)
FILE_HIERARCHY = os.path.join(FOLDER_CLASSES, "hierarchy.csv")
FILE_DATA_HIL = None # for legacy reasons, we are not able to provide company's data
FILE_GROUPING = os.path.join(FOLDER_CLASSES, "regrouping.csv")
FILE_CLEANED_DATA = os.path.join(FOLDER_DATA, "cleaned_data.csv")
FILE_ENV = os.path.join(FOLDER_DATASETS, "data.csv")
FILE_SIMILARITIES = os.path.join(FOLDER_DATA, "similarities.csv")
FILE_LIST_DISTRIBUTION = os.path.join(FOLDER_SELECTED_INDICATORS, "selection-distribution.csv")
FILE_MANUAL_ASSESSMENT = os.path.join(FOLDER_CLASSES, "manual_assessment.csv")
FILE_MANUAL_ASSESSMENT_EXPERTS = os.path.join(FOLDER_CLASSES, "iris-expertises-FabienFranckNelly.csv")
# 2. define some constants
TITLES = False # set True to display titles on plots
OLD_PREFIX, NEW_PREFIX = "old_", "new_"
RANDOM_STATE = 0 # make classifiers deterministic
TRAIN_SIZE, TEST_SIZE = 0.8, 0.2
TOPS_K = [10, 20, 30, 40, 50, 75, 100] # define top-k to generate lists of selected indicators
PREFERRED_LANGUAGE = "english"
# 4. define EV, the possible values for each one and their translation (because data come from a French company).
ENVIRONMENT_VALUES = {
"building_type": {
"Maisons": "Houses",
"Mixte": "Mixed",
"Immeubles": "Towers",
"Grand ensemble": "Housing estates",
"Lotissement": "Housing subdivisions"
},
"building_usage": {
"Résidentiel": "Housing",
"Commerçant": "Shopping",
"Autres activités": "Other activities",
},
"landscape": {
"Urbanisé": "Urban",
"Espaces verts": "Green areas",
"Arboré": "Forest",
"Agricole": "Countryside",
},
"morphological_position": {
"Central": "Central",
"Urbain": "Urban",
"Péri-urbain": "Peri-urban",
"Rural": "Rural"
},
"geographical_position": {
"Centre": "Centre",
"Nord": "North",
"Sud": "South",
"Est": "East",
"Ouest": "West",
"Nord-Est": "North East",
"Sud-Est": "South East",
"Nord-Ouest": "North West",
"Sud-Ouest": "South West"
},
"social_class": {
"Popu": "Lower",
"Moyen-inf": "Lower middle",
"Moyen": "Middle",
"Moyen-sup": "Upper middle",
"Sup": "Upper"
}
}
# names of EV, i.e. ['building_type', 'building_usage', 'landscape', 'morphological_position', 'geographical_position', 'social_class']
ENVIRONMENT_VARIABLES = list(ENVIRONMENT_VALUES.keys())
ENVIRONMENT_VARIABLES_FR = {
"building_type": "type_de_bâtiments",
"building_usage": "usage_des_bâtiments",
"landscape": "paysage",
"morphological_position": "position_morphologique",
"geographical_position": "position_géographique",
"social_class": "classe_sociale"
}
# translation of each value from French to English, ie. without the level with EV' names
TRANSLATION = {}
for ev in ENVIRONMENT_VALUES:
for key in ENVIRONMENT_VALUES[ev]:
TRANSLATION[key] = ENVIRONMENT_VALUES[ev][key]
predihood/generated_files/datasets/data_densitywith_assessment.csv deleted 100644 → 0
+ 0
− 386
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predihood/generated_files/manual-assessment/iris-expertises-Fabien.csv deleted 100644 → 0
+ 0
− 37
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code,building_type,building_usage,landscape,morphological_position,geographical_position,social_class
594780000,Houses,Housing,Countryside,Rural,North,Lower middle
622130000,Houses,Housing,Countryside,Rural,North-East,Lower middle
620010000,Houses,Housing,Countryside,Rural,North-East,Middle
628010000,Houses,Housing,Countryside,Rural,North-East,Middle
596650000,Houses,Housing,Countryside,Rural,North,Lower middle
620410801,Mixed,Housing,Urban,Central,Centre,Upper middle
626090000,Houses,Housing,Countryside,Rural,South,Lower middle
627440000,Mixed,Housing,Forest,Peri-urban,South-East,Lower middle
620410401,Mixed,Other activities,Forest,Urban,East,Middle
141181601,Mixed,Other activities,Urban,Urban,Centre,Middle
141181501,Mixed,Housing,Green areas,Urban,West,Lower
144370106,Mixed,Other activities,Urban,Peri-urban,West,Middle
143270101,Mixed,Other activities,Forest,Peri-urban,South-West,Middle
141181201,Mixed,Other activities,Green areas,Urban,South,Lower middle
141180201,Towers,Shopping,Urban,Central,Centre,Upper middle
141180402,Towers,Other activities,Forest,Peri-urban,North-East,Middle
141180102,Towers,Other activities,Green areas,Urban,Centre,Upper middle
141180202,Mixed,Housing,Urban,Urban,Centre,Upper middle
693860103,Houses,Housing,Green areas,Urban,South,Upper
141182004,Towers,Shopping,Green areas,Urban,South,Lower middle
141182006,Mixed,Housing,Green areas,Urban,South,Middle
141182007,Towers,Housing,Green areas,Urban,South-East,Lower middle
141181404,Towers,Other activities,Green areas,Urban,South,Middle
141010000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
143830000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
144880101,Houses,Housing,Forest,Urban,South-West,Middle
144880104,Houses,Housing,Green areas,Urban,South-West,Lower middle
144880103,Houses,Housing,Green areas,Urban,South-West,Lower middle
143650000,Houses,Housing,Countryside,Rural,South,Lower middle
144070000,Houses,Housing,Countryside,Rural,South,Middle
143270401,Towers,Shopping,Green areas,Urban,South-East,Lower
143270102,Mixed,Housing,Green areas,Urban,South-East,Middle
143270201,Housing estates,Housing,Urban,Urban,South-East,Lower
143270302,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270602,Housing estates,Housing,Green areas,Urban,South-East,Lower
143270603,Towers,Shopping,Urban,Urban,South-East,Lower middle
predihood/generated_files/manual-assessment/iris-expertises-Fabien2.csv deleted 100644 → 0
+ 0
− 65
View file @ 3911efd3
code,building_type,building_usage,landscape,morphological_position,geographical_position,social_class
141010000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
141180102,Towers,Other activities,Green areas,Urban,Centre,Upper middle
141180201,Towers,Shopping,Urban,Central,Centre,Upper middle
141180202,Mixed,Housing,Urban,Urban,Centre,Upper middle
141180402,Towers,Other activities,Forest,Peri-urban,North-East,Middle
141181201,Mixed,Other activities,Green areas,Urban,South,Lower middle
141181404,Towers,Other activities,Green areas,Urban,South,Middle
141181501,Mixed,Housing,Green areas,Urban,West,Lower
141181601,Mixed,Other activities,Urban,Urban,Centre,Middle
141182004,Towers,Shopping,Green areas,Urban,South,Lower middle
141182006,Mixed,Housing,Green areas,Urban,South,Middle
141182007,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270101,Mixed,Other activities,Forest,Peri-urban,South-West,Middle
143270102,Mixed,Housing,Green areas,Urban,South-East,Middle
143270201,Housing estates,Housing,Urban,Urban,South-East,Lower
143270302,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270401,Towers,Shopping,Green areas,Urban,South-East,Lower
143270602,Housing estates,Housing,Green areas,Urban,South-East,Lower
143270603,Towers,Shopping,Urban,Urban,South-East,Lower middle
143650000,Houses,Housing,Countryside,Rural,South,Lower middle
143830000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
144070000,Houses,Housing,Countryside,Rural,South,Middle
144370106,Mixed,Other activities,Urban,Peri-urban,West,Middle
144880101,Houses,Housing,Forest,Urban,South-West,Middle
144880103,Houses,Housing,Green areas,Urban,South-West,Lower middle
144880104,Houses,Housing,Green areas,Urban,South-West,Lower middle
173060101,Mixed,Housing,Green areas,Urban,South,Upper middle
173060102,Mixed,Housing,Green areas,Urban,South,Upper middle
340230101,Mixed,Housing,Green areas,Peri-urban,West,Middle
341450101,Mixed,Shopping,Urban,Urban,South,Upper middle
341540105,Housing subdivisions,Housing,Urban,Urban,Centre,Upper middle
430480000,Houses,Housing,Countryside,Rural,Centre,Lower middle
594780000,Houses,Housing,Countryside,Rural,North,Lower middle
596650000,Houses,Housing,Countryside,Rural,North,Lower middle
620010000,Houses,Housing,Countryside,Rural,North-East,Middle
620410401,Mixed,Other activities,Forest,Urban,East,Middle
620410801,Mixed,Housing,Urban,Central,Centre,Upper middle
622130000,Houses,Housing,Countryside,Rural,North-East,Lower middle
626090000,Houses,Housing,Countryside,Rural,South,Lower middle
627440000,Mixed,Housing,Forest,Peri-urban,South-East,Lower middle
628010000,Houses,Housing,Countryside,Rural,North-East,Middle
631950000,Houses,Housing,Forest,Rural,Centre,Middle
690340104,Mixed,Housing,Urban,Central,Centre,Upper middle
690440000,Houses,Housing,Green areas,Peri-urban,West,Upper
690690103,Mixed,Shopping,Urban,Urban,Centre,Middle
690760000,Houses,Housing,Forest,Peri-urban,West,Upper
691940101,Houses,Housing,Forest,Peri-urban,West,Upper
691940102,Houses,Housing,Forest,Peri-urban,West,Upper
692440201,Houses,Housing,Green areas,Peri-urban,West,Upper middle
692440202,Mixed,Shopping,Urban,Peri-urban,East,Middle
692500000,Houses,Housing,Forest,Peri-urban,West,Upper middle
692660201,Mixed,Other activities,Green areas,Central,East,Lower middle
692660302,Towers,Housing,Urban,Central,East,Lower
692660403,Towers,Housing,Urban,Urban,Centre,Lower
692660502,Towers,Shopping,Urban,Central,North,Lower
692661802,Towers,Housing,Urban,Central,East,Lower
693820204,Mixed,Shopping,Urban,Central,Centre,Upper
693830301,Towers,Other activities,Urban,Central,Centre,Middle
693860103,Houses,Housing,Green areas,Urban,South,Upper
693870704,Towers,Housing,Urban,Central,Centre,Lower middle
740100202,Mixed,Shopping,Green areas,Peri-urban,South,Middle
751010105,Houses,Other activities,Green areas,Urban,Centre,Upper
913770113,Mixed,Other activities,Urban,Central,Centre,Middle
914770107,Mixed,Other activities,Forest,Peri-urban,East,Middle
predihood/generated_files/manual-assessment/iris-expertises-FabienFranck.csv deleted 100644 → 0
+ 0
− 116
View file @ 3911efd3
code,building_type,building_usage,landscape,morphological_position,geographical_position,social_class
141010000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
141180102,Towers,Other activities,Green areas,Urban,Centre,Upper middle
141180201,Towers,Shopping,Urban,Central,Centre,Upper middle
141180202,Mixed,Housing,Urban,Urban,Centre,Upper middle
141180402,Towers,Other activities,Forest,Peri-urban,North-East,Middle
141181201,Mixed,Other activities,Green areas,Urban,South,Lower middle
141181404,Towers,Other activities,Green areas,Urban,South,Middle
141181501,Mixed,Housing,Green areas,Urban,West,Lower
141181601,Mixed,Other activities,Urban,Urban,Centre,Middle
141182004,Towers,Shopping,Green areas,Urban,South,Lower middle
141182006,Mixed,Housing,Green areas,Urban,South,Middle
141182007,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270101,Mixed,Other activities,Forest,Peri-urban,South-West,Middle
143270102,Mixed,Housing,Green areas,Urban,South-East,Middle
143270201,Housing estates,Housing,Urban,Urban,South-East,Lower
143270302,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270401,Towers,Shopping,Green areas,Urban,South-East,Lower
143270602,Housing estates,Housing,Green areas,Urban,South-East,Lower
143270603,Towers,Shopping,Urban,Urban,South-East,Lower middle
143650000,Houses,Housing,Countryside,Rural,South,Lower middle
143830000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
144070000,Houses,Housing,Countryside,Rural,South,Middle
144370106,Mixed,Other activities,Urban,Peri-urban,West,Middle
144880101,Houses,Housing,Forest,Urban,South-West,Middle
144880103,Houses,Housing,Green areas,Urban,South-West,Lower middle
144880104,Houses,Housing,Green areas,Urban,South-West,Lower middle
173060101,Mixed,Housing,Green areas,Urban,South,Upper middle
173060102,Mixed,Housing,Green areas,Urban,South,Upper middle
340230101,Mixed,Housing,Green areas,Peri-urban,West,Middle
341450101,Mixed,Shopping,Urban,Urban,South,Upper middle
341540105,Housing subdivisions,Housing,Urban,Urban,Centre,Upper middle
430480000,Houses,Housing,Countryside,Rural,Centre,Lower middle
594780000,Houses,Housing,Countryside,Rural,North,Lower middle
596650000,Houses,Housing,Countryside,Rural,North,Lower middle
620010000,Houses,Housing,Countryside,Rural,North-East,Middle
620410401,Mixed,Other activities,Forest,Urban,East,Middle
620410801,Mixed,Housing,Urban,Central,Centre,Upper middle
622130000,Houses,Housing,Countryside,Rural,North-East,Lower middle
626090000,Houses,Housing,Countryside,Rural,South,Lower middle
627440000,Mixed,Housing,Forest,Peri-urban,South-East,Lower middle
628010000,Houses,Housing,Countryside,Rural,North-East,Middle
631950000,Houses,Housing,Forest,Rural,Centre,Middle
690340104,Mixed,Housing,Urban,Central,Centre,Upper middle
690440000,Houses,Housing,Green areas,Peri-urban,West,Upper
690690103,Mixed,Shopping,Urban,Urban,Centre,Middle
690760000,Houses,Housing,Forest,Peri-urban,West,Upper
691940101,Houses,Housing,Forest,Peri-urban,West,Upper
691940102,Houses,Housing,Forest,Peri-urban,West,Upper
692440201,Houses,Housing,Green areas,Peri-urban,West,Upper middle
692440202,Mixed,Shopping,Urban,Peri-urban,East,Middle
692500000,Houses,Housing,Forest,Peri-urban,West,Upper middle
692660201,Mixed,Other activities,Green areas,Central,East,Lower middle
692660302,Towers,Housing,Urban,Central,East,Lower
692660403,Towers,Housing,Urban,Urban,Centre,Lower
692660502,Towers,Shopping,Urban,Central,North,Lower
692661802,Towers,Housing,Urban,Central,East,Lower
693820204,Mixed,Shopping,Urban,Central,Centre,Upper
693830301,Towers,Other activities,Urban,Central,Centre,Middle
693860103,Houses,Housing,Green areas,Urban,South,Upper
693870704,Towers,Housing,Urban,Central,Centre,Lower middle
740100202,Mixed,Shopping,Green areas,Peri-urban,South,Middle
751010105,Houses,Other activities,Green areas,Urban,Centre,Upper
913770113,Mixed,Other activities,Urban,Central,Centre,Middle
914770107,Mixed,Other activities,Forest,Peri-urban,East,Middle
410180101,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180102,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180201,Housing subdivisions,Housing,Urban,Peri-urban,South-East,Middle
410180301,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180602,Houses,Housing,Green areas,Peri-urban,West,Middle
410180701,Housing subdivisions,Housing,Forest,Peri-urban,East,Upper
410180801,Houses,Housing,Green areas,Peri-urban,East,Middle
410180901,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410181001,Houses,Housing,Green areas,Peri-urban,East,Middle
410181101,Housing subdivisions,Housing,Urban,Peri-urban,South-East,Middle
410181102,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410181201,Houses,Housing,Green areas,Peri-urban,North-West,Middle
410181202,Houses,Housing,Countryside,Peri-urban,North,Middle
412120000,Houses,Housing,Countryside,Peri-urban,North-West,Middle
422180102,Towers,Shopping,Urban,Central,Centre,Upper middle
422180103,Towers,Shopping,Urban,Central,Centre,Upper
422181304,Housing estates,Housing,Urban,Peri-urban,West,Lower
422181501,Towers,Shopping,Urban,Central,Centre,Middle
422181502,Towers,Shopping,Urban,Central,Centre,Middle
422182001,Mixed,Housing,Urban,Peri-urban,West,Lower middle
422182002,Housing estates,Housing,Urban,Peri-urban,West,Lower
422182003,Housing estates,Housing,Green areas,Peri-urban,West,Lower middle
422182005,Mixed,Housing,Green areas,Peri-urban,West,Lower middle
690910102,Housing estates,Housing,Green areas,Peri-urban,North,Lower
690910201,Houses,Housing,Urban,Urban,North,Lower
690910202,Mixed,Other activities,Urban,Urban,North,Lower
691360000,Mixed,Housing,Countryside,Rural,North,Middle
691790000,Houses,Housing,Countryside,Rural,North,Middle
692590104,Houses,Housing,Urban,Peri-urban,South-West,Lower middle
692590301,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590302,Housing estates,Housing,Green areas,Peri-urban,South-West,Lower
692590303,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590402,Housing estates,Housing,Green areas,Peri-urban,South-West,Lower
692590403,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590501,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590502,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
693810101,Towers,Shopping,Urban,Central,Centre,Upper
693820101,Towers,Shopping,Urban,Central,Centre,Upper
693820102,Towers,Shopping,Urban,Central,Centre,Upper
693860301,Towers,Housing,Urban,Central,Centre,Upper
693860401,Towers,Housing,Urban,Central,Centre,Upper
693860402,Towers,Shopping,Urban,Central,Centre,Upper
693860403,Towers,Shopping,Urban,Central,Centre,Upper
693860501,Towers,Housing,Urban,Central,Centre,Upper
693860502,Towers,Housing,Urban,Central,Centre,Upper
693860503,Towers,Housing,Urban,Central,Centre,Upper
693880101,Towers,Shopping,Urban,Central,Centre,Upper middle
693880102,Towers,Housing,Urban,Central,Centre,Upper middle
693880203,Towers,Shopping,Urban,Central,Centre,Upper
693890606,Housing estates,Housing,Green areas,Peri-urban,East,Lower
693890607,Housing estates,Housing,Urban,Peri-urban,East,Lower
predihood/generated_files/manual-assessment/iris-expertises-FabienFranckNelly.csv deleted 100644 → 0
+ 0
− 118
View file @ 3911efd3
code,building_type,building_usage,landscape,morphological_position,geographical_position,social_class
141010000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
141180102,Towers,Other activities,Green areas,Urban,Centre,Upper middle
141180201,Towers,Shopping,Urban,Central,Centre,Upper middle
141180202,Mixed,Housing,Urban,Urban,Centre,Upper middle
141180402,Towers,Other activities,Forest,Peri-urban,North-East,Middle
141181201,Mixed,Other activities,Green areas,Urban,South,Lower middle
141181404,Towers,Other activities,Green areas,Urban,South,Middle
141181501,Mixed,Housing,Green areas,Urban,West,Lower
141181601,Mixed,Other activities,Urban,Urban,Centre,Middle
141182004,Towers,Shopping,Green areas,Urban,South,Lower middle
141182006,Mixed,Housing,Green areas,Urban,South,Middle
141182007,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270101,Mixed,Other activities,Forest,Peri-urban,South-West,Middle
143270102,Mixed,Housing,Green areas,Urban,South-East,Middle
143270201,Housing estates,Housing,Urban,Urban,South-East,Lower
143270302,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270401,Towers,Shopping,Green areas,Urban,South-East,Lower
143270602,Housing estates,Housing,Green areas,Urban,South-East,Lower
143270603,Towers,Shopping,Urban,Urban,South-East,Lower middle
143650000,Houses,Housing,Countryside,Rural,South,Lower middle
143830000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
144070000,Houses,Housing,Countryside,Rural,South,Middle
144370106,Mixed,Other activities,Urban,Peri-urban,West,Middle
144880101,Houses,Housing,Forest,Urban,South-West,Middle
144880103,Houses,Housing,Green areas,Urban,South-West,Lower middle
144880104,Houses,Housing,Green areas,Urban,South-West,Lower middle
173060101,Mixed,Housing,Green areas,Urban,South,Upper middle
173060102,Mixed,Housing,Green areas,Urban,South,Upper middle
340230101,Mixed,Housing,Green areas,Peri-urban,West,Middle
341450101,Mixed,Shopping,Urban,Urban,South,Upper middle
341540105,Housing subdivisions,Housing,Urban,Urban,Centre,Upper middle
430480000,Houses,Housing,Countryside,Rural,Centre,Lower middle
594780000,Houses,Housing,Countryside,Rural,North,Lower middle
596650000,Houses,Housing,Countryside,Rural,North,Lower middle
620010000,Houses,Housing,Countryside,Rural,North-East,Middle
620410401,Mixed,Other activities,Forest,Urban,East,Middle
620410801,Mixed,Housing,Urban,Central,Centre,Upper middle
622130000,Houses,Housing,Countryside,Rural,North-East,Lower middle
626090000,Houses,Housing,Countryside,Rural,South,Lower middle
627440000,Mixed,Housing,Forest,Peri-urban,South-East,Lower middle
628010000,Houses,Housing,Countryside,Rural,North-East,Middle
631950000,Houses,Housing,Forest,Rural,Centre,Middle
690340104,Mixed,Housing,Urban,Central,Centre,Upper middle
690440000,Houses,Housing,Green areas,Peri-urban,West,Upper
690690103,Mixed,Shopping,Urban,Urban,Centre,Middle
690760000,Houses,Housing,Forest,Peri-urban,West,Upper
691940101,Houses,Housing,Forest,Peri-urban,West,Upper
691940102,Houses,Housing,Forest,Peri-urban,West,Upper
692440201,Houses,Housing,Green areas,Peri-urban,West,Upper middle
692440202,Mixed,Shopping,Urban,Peri-urban,East,Middle
692500000,Houses,Housing,Forest,Peri-urban,West,Upper middle
692660201,Mixed,Other activities,Green areas,Central,East,Lower middle
692660302,Towers,Housing,Urban,Central,East,Lower
692660403,Towers,Housing,Urban,Urban,Centre,Lower
692660502,Towers,Shopping,Urban,Central,North,Lower
692661802,Towers,Housing,Urban,Central,East,Lower
693820204,Mixed,Shopping,Urban,Central,Centre,Upper
693830301,Towers,Other activities,Urban,Central,Centre,Middle
693860103,Houses,Housing,Green areas,Urban,South,Upper
693870704,Towers,Housing,Urban,Central,Centre,Lower middle
740100202,Mixed,Shopping,Green areas,Peri-urban,South,Middle
751010105,Houses,Other activities,Green areas,Urban,Centre,Upper
913770113,Mixed,Other activities,Urban,Central,Centre,Middle
914770107,Mixed,Other activities,Forest,Peri-urban,East,Middle
410180101,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180102,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180201,Housing subdivisions,Housing,Urban,Peri-urban,South-East,Middle
410180301,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180602,Houses,Housing,Green areas,Peri-urban,West,Middle
410180701,Housing subdivisions,Housing,Forest,Peri-urban,East,Upper
410180801,Houses,Housing,Green areas,Peri-urban,East,Middle
410180901,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410181001,Houses,Housing,Green areas,Peri-urban,East,Middle
410181101,Housing subdivisions,Housing,Urban,Peri-urban,South-East,Middle
410181102,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410181201,Houses,Housing,Green areas,Peri-urban,North-West,Middle
410181202,Houses,Housing,Countryside,Peri-urban,North,Middle
412120000,Houses,Housing,Countryside,Peri-urban,North-West,Middle
422180102,Towers,Shopping,Urban,Central,Centre,Upper middle
422180103,Towers,Shopping,Urban,Central,Centre,Upper
422181304,Housing estates,Housing,Urban,Peri-urban,West,Lower
422181501,Towers,Shopping,Urban,Central,Centre,Middle
422181502,Towers,Shopping,Urban,Central,Centre,Middle
422182001,Mixed,Housing,Urban,Peri-urban,West,Lower middle
422182002,Housing estates,Housing,Urban,Peri-urban,West,Lower
422182003,Housing estates,Housing,Green areas,Peri-urban,West,Lower middle
422182005,Mixed,Housing,Green areas,Peri-urban,West,Lower middle
690910102,Housing estates,Housing,Green areas,Peri-urban,North,Lower
690910201,Houses,Housing,Urban,Urban,North,Lower
690910202,Mixed,Other activities,Urban,Urban,North,Lower
691360000,Mixed,Housing,Countryside,Rural,North,Middle
691790000,Houses,Housing,Countryside,Rural,North,Middle
692590104,Houses,Housing,Urban,Peri-urban,South-West,Lower middle
692590301,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590302,Housing estates,Housing,Green areas,Peri-urban,South-West,Lower
692590303,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590402,Housing estates,Housing,Green areas,Peri-urban,South-West,Lower
692590403,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590501,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590502,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
693810101,Towers,Shopping,Urban,Central,Centre,Upper
693820101,Towers,Shopping,Urban,Central,Centre,Upper
693820102,Towers,Shopping,Urban,Central,Centre,Upper
693860301,Towers,Housing,Urban,Central,Centre,Upper
693860401,Towers,Housing,Urban,Central,Centre,Upper
693860402,Towers,Shopping,Urban,Central,Centre,Upper
693860403,Towers,Shopping,Urban,Central,Centre,Upper
693860501,Towers,Housing,Urban,Central,Centre,Upper
693860502,Towers,Housing,Urban,Central,Centre,Upper
693860503,Towers,Housing,Urban,Central,Centre,Upper
693880101,Towers,Shopping,Urban,Central,Centre,Upper middle
693880102,Towers,Housing,Urban,Central,Centre,Upper middle
693880203,Towers,Shopping,Urban,Central,Centre,Upper
693890606,Housing estates,Housing,Green areas,Peri-urban,East,Lower
693890607,Housing estates,Housing,Urban,Peri-urban,East,Lower
31020000,Houses,Housing,Forest,Peri-urban,East,Middle
13800000,Houses,Housing,Countryside,Rural,Centre,Lower middle
predihood/generated_files/manual-assessment/iris-expertises-Franck.csv deleted 100644 → 0
+ 0
− 52
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code,building_type,building_usage,landscape,morphological_position,geographical_position,social_class
410180101,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180102,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180201,Housing subdivisions,Housing,Urban,Peri-urban,South-East,Middle
410180301,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180602,Houses,Housing,Green areas,Peri-urban,West,Middle
410180701,Housing subdivisions,Housing,Forest,Peri-urban,East,Upper
410180801,Houses,Housing,Green areas,Peri-urban,East,Middle
410180901,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410181001,Houses,Housing,Green areas,Peri-urban,East,Middle
410181101,Housing subdivisions,Housing,Urban,Peri-urban,South-East,Middle
410181102,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410181201,Houses,Housing,Green areas,Peri-urban,North-West,Middle
410181202,Houses,Housing,Countryside,Peri-urban,North,Middle
412120000,Houses,Housing,Countryside,Peri-urban,North-West,Middle
422180102,Towers,Shopping,Urban,Central,Centre,Upper middle
422180103,Towers,Shopping,Urban,Central,Centre,Upper
422181304,Housing estates,Housing,Urban,Peri-urban,West,Lower
422181501,Towers,Shopping,Urban,Central,Centre,Middle
422181502,Towers,Shopping,Urban,Central,Centre,Middle
422182001,Mixed,Housing,Urban,Peri-urban,West,Lower middle
422182002,Housing estates,Housing,Urban,Peri-urban,West,Lower
422182003,Housing estates,Housing,Green areas,Peri-urban,West,Lower middle
422182005,Mixed,Housing,Green areas,Peri-urban,West,Lower middle
690910102,Housing estates,Housing,Green areas,Peri-urban,North,Lower
690910201,Houses,Housing,Urban,Urban,North,Lower
690910202,Mixed,Other activities,Urban,Urban,North,Lower
691360000,Mixed,Housing,Countryside,Rural,North,Middle
691790000,Houses,Housing,Countryside,Rural,North,Middle
692590104,Houses,Housing,Urban,Peri-urban,South-West,Lower middle
692590301,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590302,Housing estates,Housing,Green areas,Peri-urban,South-West,Lower
692590303,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590402,Housing estates,Housing,Green areas,Peri-urban,South-West,Lower
692590403,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590501,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590502,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
693810101,Towers,Shopping,Urban,Central,Centre,Upper
693820101,Towers,Shopping,Urban,Central,Centre,Upper
693820102,Towers,Shopping,Urban,Central,Centre,Upper
693860301,Towers,Housing,Urban,Central,Centre,Upper
693860401,Towers,Housing,Urban,Central,Centre,Upper
693860402,Towers,Shopping,Urban,Central,Centre,Upper
693860403,Towers,Shopping,Urban,Central,Centre,Upper
693860501,Towers,Housing,Urban,Central,Centre,Upper
693860502,Towers,Housing,Urban,Central,Centre,Upper
693860503,Towers,Housing,Urban,Central,Centre,Upper
693880101,Towers,Shopping,Urban,Central,Centre,Upper middle
693880102,Towers,Housing,Urban,Central,Centre,Upper middle
693880203,Towers,Shopping,Urban,Central,Centre,Upper
693890606,Housing estates,Housing,Green areas,Peri-urban,East,Lower
693890607,Housing estates,Housing,Urban,Peri-urban,East,Lower
predihood/generated_files/manual-assessment/iris-expertises-Nelly.csv deleted 100644 → 0
+ 0
− 31
View file @ 3911efd3
code,building_type,building_usage,landscape,morphological_position,geographical_position,social_class
692440201,Houses,Housing,Green areas,Peri-urban,West,Upper middle
690760000,Houses,Housing,Forest,Peri-urban,West,Upper
31020000,Houses,Housing,Forest,Peri-urban,East,Middle
692661802,Towers,Housing,Urban,Central,East,Lower
692660302,Towers,Housing,Urban,Central,East,Lower
692660201,Mixed,Other activities,Green areas,Central,East,Lower middle
692440202,Mixed,Shopping,Urban,Peri-urban,East,Middle
693830301,Towers,Other activities,Urban,Central,Centre,Middle
693820204,Mixed,Shopping,Urban,Central,Centre,Upper
631950000,Houses,Housing,Forest,Rural,Centre,Middle
690440000,Houses,Housing,Green areas,Peri-urban,West,Upper
693870704,Towers,Housing,Urban,Central,Centre,Lower middle
430480000,Houses,Housing,Countryside,Rural,Centre,Lower middle
341540105,Housing subdivisions,Housing,Urban,Urban,Centre,Upper middle
914770107,Mixed,Other activities,Forest,Peri-urban,East,Middle
913770113,Mixed,Other activities,Urban,Central,Centre,Middle
173060102,Mixed,Housing,Green areas,Urban,South,Upper middle
173060101,Mixed,Housing,Green areas,Urban,South,Upper middle
341450101,Mixed,Shopping,Urban,Urban,South,Upper middle
340230101,Mixed,Housing,Green areas,Peri-urban,West,Middle
740100202,Mixed,Shopping,Green areas,Peri-urban,South,Middle
751010105,Houses,Other activities,Green areas,Urban,Centre,Upper
692660502,Towers,Shopping,Urban,Central,North,Lower
692660403,Towers,Housing,Urban,Urban,Centre,Lower
690340104,Mixed,Housing,Urban,Central,Centre,Upper middle
691940102,Houses,Housing,Forest,Peri-urban,West,Upper
691940101,Houses,Housing,Forest,Peri-urban,West,Upper
692500000,Houses,Housing,Forest,Peri-urban,West,Upper middle
690690103,Mixed,Shopping,Urban,Urban,Centre,Middle
13800000,Houses,Housing,Countryside,Rural,Centre,Lower middle
predihood/generated_files/manual-assessment/manual_assessment.csv deleted 100644 → 0
+ 0
− 136
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This diff is collapsed.
predihood/generated_files/manual-assessment/manual_assessment_experts.csv deleted 100644 → 0
+ 0
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code,building_type,building_usage,landscape,morphological_position,geographical_position,social_class
13800000,Houses,Housing,Countryside,Rural,Centre,Lower middle
31020000,Houses,Housing,Forest,Peri-urban,East,Middle
141010000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
141180102,Towers,Other activities,Green areas,Urban,Centre,Upper middle
141180201,Towers,Shopping,Urban,Central,Centre,Upper middle
141180202,Mixed,Housing,Urban,Urban,Centre,Upper middle
141180402,Towers,Other activities,Forest,Peri-urban,North-East,Middle
141181201,Mixed,Other activities,Green areas,Urban,South,Lower middle
141181404,Towers,Other activities,Green areas,Urban,South,Middle
141181501,Mixed,Housing,Green areas,Urban,West,Lower
141181601,Mixed,Other activities,Urban,Urban,Centre,Middle
141182004,Towers,Shopping,Green areas,Urban,South,Lower middle
141182006,Mixed,Housing,Green areas,Urban,South,Middle
141182007,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270101,Mixed,Other activities,Forest,Peri-urban,South-West,Middle
143270102,Mixed,Housing,Green areas,Urban,South-East,Middle
143270201,Housing estates,Housing,Urban,Urban,South-East,Lower
143270302,Towers,Housing,Green areas,Urban,South-East,Lower middle
143270401,Towers,Shopping,Green areas,Urban,South-East,Lower
143270602,Housing estates,Housing,Green areas,Urban,South-East,Lower
143270603,Towers,Shopping,Urban,Urban,South-East,Lower middle
143650000,Houses,Housing,Countryside,Rural,South,Lower middle
143830000,Housing subdivisions,Housing,Countryside,Peri-urban,North-East,Middle
144070000,Houses,Housing,Countryside,Rural,South,Middle
144370106,Mixed,Other activities,Urban,Peri-urban,West,Middle
144880101,Houses,Housing,Forest,Urban,South-West,Middle
144880103,Houses,Housing,Green areas,Urban,South-West,Lower middle
144880104,Houses,Housing,Green areas,Urban,South-West,Lower middle
173060101,Mixed,Housing,Green areas,Urban,South,Upper middle
173060102,Mixed,Housing,Green areas,Urban,South,Upper middle
340230101,Mixed,Housing,Green areas,Peri-urban,West,Middle
341450101,Mixed,Shopping,Urban,Urban,South,Upper middle
341540105,Housing subdivisions,Housing,Urban,Urban,Centre,Upper middle
410180101,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180102,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180201,Housing subdivisions,Housing,Urban,Peri-urban,South-East,Middle
410180301,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410180602,Houses,Housing,Green areas,Peri-urban,West,Middle
410180701,Housing subdivisions,Housing,Forest,Peri-urban,East,Upper
410180801,Houses,Housing,Green areas,Peri-urban,East,Middle
410180901,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410181001,Houses,Housing,Green areas,Peri-urban,East,Middle
410181101,Housing subdivisions,Housing,Urban,Peri-urban,South-East,Middle
410181102,Housing estates,Housing,Urban,Peri-urban,North-East,Lower
410181201,Houses,Housing,Green areas,Peri-urban,North-West,Middle
410181202,Houses,Housing,Countryside,Peri-urban,North,Middle
412120000,Houses,Housing,Countryside,Peri-urban,North-West,Middle
422180102,Towers,Shopping,Urban,Central,Centre,Upper middle
422180103,Towers,Shopping,Urban,Central,Centre,Upper
422181304,Housing estates,Housing,Urban,Peri-urban,West,Lower
422181501,Towers,Shopping,Urban,Central,Centre,Middle
422181502,Towers,Shopping,Urban,Central,Centre,Middle
422182001,Mixed,Housing,Urban,Peri-urban,West,Lower middle
422182002,Housing estates,Housing,Urban,Peri-urban,West,Lower
422182003,Housing estates,Housing,Green areas,Peri-urban,West,Lower middle
422182005,Mixed,Housing,Green areas,Peri-urban,West,Lower middle
430480000,Houses,Housing,Countryside,Rural,Centre,Lower middle
594780000,Houses,Housing,Countryside,Rural,North,Lower middle
596650000,Houses,Housing,Countryside,Rural,North,Lower middle
620010000,Houses,Housing,Countryside,Rural,North-East,Middle
620410401,Mixed,Other activities,Forest,Urban,East,Middle
620410801,Mixed,Housing,Urban,Central,Centre,Upper middle
622130000,Houses,Housing,Countryside,Rural,North-East,Lower middle
626090000,Houses,Housing,Countryside,Rural,South,Lower middle
627440000,Mixed,Housing,Forest,Peri-urban,South-East,Lower middle
628010000,Houses,Housing,Countryside,Rural,North-East,Middle
631950000,Houses,Housing,Forest,Rural,Centre,Middle
690340104,Mixed,Housing,Urban,Central,Centre,Upper middle
690440000,Houses,Housing,Green areas,Peri-urban,West,Upper
690690103,Mixed,Shopping,Urban,Urban,Centre,Middle
690760000,Houses,Housing,Forest,Peri-urban,West,Upper
690910102,Housing estates,Housing,Green areas,Peri-urban,North,Lower
690910201,Houses,Housing,Urban,Urban,North,Lower
690910202,Mixed,Other activities,Urban,Urban,North,Lower
691360000,Mixed,Housing,Countryside,Rural,North,Middle
691790000,Houses,Housing,Countryside,Rural,North,Middle
691940101,Houses,Housing,Forest,Peri-urban,West,Upper
691940102,Houses,Housing,Forest,Peri-urban,West,Upper
692440201,Houses,Housing,Green areas,Peri-urban,West,Upper middle
692440202,Mixed,Shopping,Urban,Peri-urban,East,Middle
692500000,Houses,Housing,Forest,Peri-urban,West,Upper middle
692590104,Houses,Housing,Urban,Peri-urban,South-West,Lower middle
692590301,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590302,Housing estates,Housing,Green areas,Peri-urban,South-West,Lower
692590303,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590402,Housing estates,Housing,Green areas,Peri-urban,South-West,Lower
692590403,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590501,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692590502,Housing estates,Housing,Urban,Peri-urban,South-West,Lower
692660201,Mixed,Other activities,Green areas,Central,East,Lower middle
692660302,Towers,Housing,Urban,Central,East,Lower
692660403,Towers,Housing,Urban,Urban,Centre,Lower
692660502,Towers,Shopping,Urban,Central,North,Lower
692661802,Towers,Housing,Urban,Central,East,Lower
693810101,Towers,Shopping,Urban,Central,Centre,Upper
693820101,Towers,Shopping,Urban,Central,Centre,Upper
693820102,Towers,Shopping,Urban,Central,Centre,Upper
693820204,Mixed,Shopping,Urban,Central,Centre,Upper
693830301,Towers,Other activities,Urban,Central,Centre,Middle
693860103,Houses,Housing,Green areas,Urban,South,Upper
693860301,Towers,Housing,Urban,Central,Centre,Upper
693860401,Towers,Housing,Urban,Central,Centre,Upper
693860402,Towers,Shopping,Urban,Central,Centre,Upper
693860403,Towers,Shopping,Urban,Central,Centre,Upper
693860501,Towers,Housing,Urban,Central,Centre,Upper
693860502,Towers,Housing,Urban,Central,Centre,Upper
693860503,Towers,Housing,Urban,Central,Centre,Upper
693870704,Towers,Housing,Urban,Central,Centre,Lower middle
693880101,Towers,Shopping,Urban,Central,Centre,Upper middle
693880102,Towers,Housing,Urban,Central,Centre,Upper middle
693880203,Towers,Shopping,Urban,Central,Centre,Upper
693890606,Housing estates,Housing,Green areas,Peri-urban,East,Lower
693890607,Housing estates,Housing,Urban,Peri-urban,East,Lower
740100202,Mixed,Shopping,Green areas,Peri-urban,South,Middle
751010105,Houses,Other activities,Green areas,Urban,Centre,Upper
913770113,Mixed,Other activities,Urban,Central,Centre,Middle
914770107,Mixed,Other activities,Forest,Peri-urban,East,Middle
\ No newline at end of file
predihood/generated_files/selected-indicators/list10.csv deleted 100644 → 0
+ 0
− 6
View file @ 3911efd3
0,P14_ACTOCC15P_ILT2,P14_RPMAISON_ACHTOT,P14_PMEN_ANEM0509,P14_MAISON,AREA,P14_RPMAISON,C14_PMEN_CS3,P14_POP,P14_RPAPPART_ACH19,C14_ACT1564_CS3,P14_ACTOCC15P_ILT2P,C14_POP15P_CS3,P14_NBPI_RPMAISON,DENSITY,C14_ACTOCC15P_DROU,P14_ANEM_RP_PROP
1,P14_RP_ACH45,P14_RP_ACH90,P14_RP_PROP,NB_A504,C14_PMEN_CS6,AREA,P14_NPER_RP_PROP,C14_PMEN_CS3,P14_POP,C14_ACT1564_CS3,C14_PMEN_MENSFAM,C14_POP15P_CS3,P14_APPART,P14_RP_LOCHLMV,DENSITY,P14_POP_IMM
2,P14_POPMEN5579,DEC_D814,P14_RPMAISON_ACH90,AREA,C14_NE24F2,P14_NPER_RP_PROP,C14_MENSFAM,P14_POP,C14_PMEN_MENSFAM,DEC_Q114,DEC_D414,C14_PMEN_CS7,DENSITY,P14_POP1524_PSEUL,DEC_D114,P14_ANEM_RP_PROP
3,C14_PMEN_MENFAM,P14_POP0610,P14_RPMAISON_ACH90,AREA,P14_POP1824,P14_RPAPPART_ACH19,P14_POP,C14_ACT1564_CS2,P14_ACTOCC15P_ILT2P,P14_APPART,P14_ANEM_RP_LOCHLMV,P14_NBPI_RPMAISON,DENSITY,P14_RP_ACH19,P14_RP_1P,P14_POP_IMM
4,P14_NPER_RP_LOCHLMV,P14_ANEM_RP_LOC,AREA,P14_ACTOCC15P_ILT3,P14_RPAPPART_ACH05,C14_ACTOCC15P_TCOM,P14_POP,P14_RPAPPART_ACH11,P14_POP1524_PSEUL,C14_POP15P_CS3,P14_ANEM_RP_LOCHLMV,P14_RP_CCCOLL,DENSITY,P14_RP_ACH19,,
5,DEC_D814,DEC_D914,P14_NPER_RP_LOC,P14_RP_CINDELEC,AREA,P14_RPAPPART_ACH19,P14_POP,C14_PMEN_CS3,C14_PMEN_MENSFAM,C14_MEN_CS3,C14_MEN_CS8,P14_ANEM_RP_LOCHLMV,DENSITY,P14_RP_ACH19,P14_RP_1P,P14_POP_IMM
predihood/generated_files/selected-indicators/list100.csv deleted 100644 → 0
+ 0
− 6
View file @ 3911efd3
0,P14_POP0002,P14_POPMEN2554,P14_RP_6080M2,P14_ACTOCC15P_ILT3,P14_NSAL15P_INDEP,P14_RPAPPART_ACH70,P14_RP_3040M2,P14_ANEM_RP,P14_NBPI_RP_ANEM10P,P14_POP0610,AREA,P14_POP1824,P14_NSCOL15P_SUP,P14_LOG,DENSITY,C14_MENPSEUL,P14_POP2539,P14_POPMEN5579,C14_NE24F4P,P14_RPMAISON,P14_POP,C14_NE24F3,P14_SCOL1824,DEC_D614,P14_POP1564,P14_ANEM_RP_LOCHLMV,P14_RP_LOCHLMV,C14_MEN_CS7,P14_SCOL1114,DEC_D814,DEC_D914,P14_NPER_RP_PROP,C14_ACTOCC15P_MAR,P14_APPART,P14_POP1529,P14_RP_ACH05,DEC_D114,P14_NPER_RP_LOCHLMV,C14_MENHSEUL,P14_POP15P_MARIEE,P14_RPAPPART_ACH19,P14_SAL15P_EMPAID,DEC_D414,P14_ACTOCC15P_ILT5,P14_RP_CCCOLL,C14_PMEN_CS3,P14_RP_ACH19,P14_INACT1564,P14_ANEM_RP_PROP,P14_CHOM1564,P14_ACTOCC15P_ILT2,P14_RP_2P,P14_PMEN_ANEM0509,P14_MAISON,P14_RP_VOIT1P,P14_RP_ACH70,P14_POP2554,P14_ACTOCC15P_ILT2P,P14_RP_M30M2,P14_RP_PROP,C14_FAM,P14_POP15P_NONMARIEE,P14_NPER_RP_GRAT,P14_POP1524,P14_RPAPPART_ACHTOT,P14_PMEN_ANEM10P,C14_ACTOCC15P_DROU,C14_PMEN_MENFAM,P14_RP_ACH90,P14_NBPI_RP,C14_PMEN_CS6,C14_POP15P_CS2,C14_MENFSEUL,C14_NE24F2,C14_ACT1564_CS3,DEC_D214,P14_ACTOCC15P_ILT1,P14_POP_IMM,P14_RSECOCC,P14_RP_CINDELEC,P14_ACTOCC1564,C14_ACT1564_CS5,P14_POP4559,C14_PMEN_CS5,P14_NBPI_RPMAISON,P14_POP1524_PSEUL,P14_RP_ACH45,NB_A504,P14_NPER_RP_LOC,C14_PMEN_CS8,C14_PMEN_CS4,C14_ACTOCC15P,C14_MEN_CS6,P14_ACT1524,C14_PMEN_CS2,P14_RPAPPART,P14_RPMAISON_ACHTOT,P14_RPMAISON_ACH90,P14_POP5564,P14_NBPI_RPAPPART,C14_COUPAENF,C14_POP15P_CS5,C14_MENCOUPAENF,P14_ANEM_RP_GRAT,C14_ACTOCC15P_PAS,P14_POP30P,P14_RPAPPART_ACH05,P14_RP_GARL,C14_MEN_CS5,P14_POP0019,P14_ACT2554,P14_RP_LOC,C14_PMEN,C14_ACT1564_CS4,P14_NSCOL15P_CAPBEP,C14_PMEN_MENCOUPSENF,P14_POP0205,C14_POP15P_CS8,C14_ACTOCC1564_CS3,DEC_D314,P14_ACTOCC15P_TP,P14_RPAPPART_ACH90,P14_ACTOCC15P_ILT4,P14_SAL15P_APPR,P14_POP15P,C14_PMEN_MENCOUPAENF,DEC_D714,C14_MEN_CS3,DEC_Q314,P14_NSCOL15P,P14_PMEN_ANEM0204,P14_RPAPPART_ACH45,P14_RP_1P,P14_RPMAISON_ACH05,P14_CHOM2554,C14_MENSFAM,P14_RP_CCIND,C14_MEN_CS8,P14_RPAPPART_ACH11,P14_RP_80100M2,C14_POP15P_CS3,P14_RPMAISON_ACH45,C14_ACTOCC1564_CS2,C14_ACT1564_CS2,P14_RP_5PP,P14_SCOL1517,,,
1,P14_POPMEN2554,P14_RP_6080M2,P14_PMEN_ANEM0002,P14_ACTOCC15P_ILT3,P14_PMEN,DEC_Q114,P14_RPAPPART_ACH70,P14_SCOL0205,P14_RP_3040M2,C14_ACT1564_CS6,P14_ANEM_RP,P14_RP_3P,C14_MEN_CS4,P14_POP0610,AREA,P14_MEN_ANEM0002,P14_NSCOL15P_SUP,P14_POP6074,P14_LOG,DENSITY,P14_POP0014,C14_MENPSEUL,P14_POPMEN1524,P14_POP2539,P14_POPMEN5579,P14_RPMAISON,P14_POP,C14_MENCOUPSENF,P14_SCOL1824,P14_MEN_ANEM0204,P14_POP1564,P14_ANEM_RP_LOCHLMV,P14_RP_LOCHLMV,P14_LOGVAC,C14_ACTOCC1564,P14_POP4054,P14_SCOL1114,DEC_D814,DEC_D914,P14_NPER_RP_PROP,P14_POP0305,P14_NSCOL15P_DIPLMIN,P14_APPART,P14_RP_ACH05,P14_ACTOCC2554,DEC_D114,P14_NPER_RP_LOCHLMV,C14_POP15P_CS6,P14_RPAPPART_ACH19,P14_POP15P_MARIEE,DEC_D414,P14_RP_CCCOLL,C14_PMEN_CS3,P14_RP_ACH19,P14_PHORMEN,P14_ANEM_RP_PROP,P14_SCOL2529,P14_ACTOCC15P_ILT2,P14_RP_2P,P14_PMEN_ANEM0509,P14_RP_VOIT1P,P14_SAL15P_CDI,P14_ACTOCC15P_ILT2P,P14_NBPI_RP_ANEM0509,P14_RP,P14_RP_M30M2,P14_AINACT1564,C14_PMEN_MENFAMMONO,P14_RP_PROP,C14_FAM,C14_ACTOCC1564_CS6,C14_FAMMONO,P14_POP1524,P14_RPAPPART_ACHTOT,P14_POP_FR,P14_PMEN_ANEM10P,C14_ACTOCC15P_DROU,C14_PMEN_MENFAM,P14_RP_ACH90,P14_NBPI_RP,C14_PMEN_CS6,C14_MENFSEUL,C14_POP15P_CS7,C14_ACT1564_CS3,P14_F1529,P14_RP_ACH11,DEC_D214,P14_ACTOCC15P_ILT1,P14_POP_IMM,P14_RSECOCC,P14_RP_CINDELEC,C14_NE24F1,C14_ACT1564_CS5,P14_POP4559,C14_PMEN_CS5,P14_POP1524_PSEUL,P14_RP_ACH45,NB_A504,P14_NPER_RP_LOC,P14_RPMAISON_ACH70,C14_ACTOCC15P,DEC_MED14,C14_MEN_CS6,P14_RPAPPART,P14_RPMAISON_ACHTOT,C14_PMEN_CS2,P14_NBPI_RPAPPART,P14_RPMAISON_ACH90,P14_POP2064,C14_COUPAENF,P14_POP_ETR,C14_MENCOUPAENF,P14_ANEM_RP_GRAT,P14_ANEM_RP_LOC,C14_ACTOCC15P_PAS,P14_POP30P,P14_RPAPPART_ACH05,P14_ACTOCC15P,P14_RP_GARL,P14_POP0019,C14_POP15P_CS4,P14_RP_LOC,P14_POP65P,P14_NSCOL15P_CAPBEP,C14_POP15P_CS8,C14_ACTOCC1564_CS3,DEC_D314,P14_MEN_ANEM0509,P14_RPAPPART_ACH90,P14_POP15P,C14_PMEN_MENCOUPAENF,C14_MEN_CS3,DEC_Q314,P14_NSCOL15P,P14_RP_GRAT,P14_PMEN_ANEM0204,P14_RPAPPART_ACH45,DEC_PTSA14,P14_RP_1P,C14_MENSFAM,P14_RP_4060M2,P14_RPMAISON_ACH45,C14_POP15P_CS3,C14_PMEN_MENSFAM,P14_NBPI_RP_ANEM0204,DEC_TP6014,,,
2,P14_POP0002,P14_RP_6080M2,P14_PMEN_ANEM0002,P14_PMEN,DEC_Q114,P14_RPAPPART_ACH70,P14_RP_3040M2,P14_ANEM_RP,P14_POP0610,P14_NSAL15P,AREA,P14_POP1824,P14_NSCOL15P_SUP,DENSITY,P14_POP0014,C14_MENPSEUL,P14_POPMEN1524,P14_POPMEN5579,P14_RP_VOIT2P,P14_RPMAISON,P14_POP,P14_SCOL1824,DEC_D614,P14_MEN_ANEM0204,P14_ANEM_RP_LOCHLMV,P14_RP_LOCHLMV,C14_MEN_CS7,C14_ACTOCC1564,P14_ACTOCC1524,DEC_D914,P14_POPMEN80P,DEC_D814,P14_RP_4P,P14_POP1114,P14_NPER_RP_PROP,P14_F3044,C14_ACTOCC15P_MAR,P14_APPART,DEC_D114,P14_NPER_RP_LOCHLMV,P14_RP_VOIT1,C14_MENHSEUL,P14_POP15P_MARIEE,P14_RPAPPART_ACH19,P14_POP1517,P14_POP2529,DEC_D414,C14_PMEN_CS7,C14_PMEN_CS3,P14_RP_ACH19,P14_RP_CCCOLL,P14_ANEM_RP_PROP,P14_ACTOCC15P_ILT2,P14_RP_2P,P14_PMEN_ANEM0509,P14_RP_ACH70,P14_POP2554,P14_SAL15P_CDI,P14_ACTOCC15P_ILT2P,NB_F101,P14_RP_M30M2,P14_RP_PROP,P14_ACTOCC5564,C14_FAM,P14_POP15P_NONMARIEE,C14_NE24F0,C14_ACTOCC1564_CS6,P14_NPER_RP_GRAT,P14_RPAPPART_ACHTOT,P14_POP1524,P14_POPMEN15P,P14_POP_FR,P14_PMEN_ANEM10P,C14_PMEN_MENFAM,P14_NBPI_RP,P14_RP_ACH90,C14_POP15P_CS2,C14_NE24F2,C14_POP15P_CS7,C14_ACT1564_CS3,DEC_D214,P14_NSCOL15P_BAC,P14_POP_IMM,P14_RSECOCC,P14_RP_CINDELEC,P14_NBPI_RPMAISON,P14_POP1524_PSEUL,P14_POP80P,P14_RP_ACH45,NB_A504,P14_POP5579,P14_NPER_RP_LOC,P14_SAL15P_CDD,P14_RPMAISON_ACH70,C14_PMEN_CS8,C14_PMEN_CS4,C14_ACTOCC15P,DEC_MED14,P14_ACT1524,P14_RPMAISON_ACHTOT,P14_RPAPPART,C14_PMEN_CS2,P14_RPMAISON_ACH90,P14_NBPI_RPAPPART,P14_POP_ETR,C14_POP15P_CS5,P14_ANEM_RP_GRAT,C14_MENCOUPAENF,C14_ACTOCC1564_CS5,P14_ANEM_RP_LOC,P14_POP30P,P14_RPAPPART_ACH05,C14_ACTOCC15P_TCOM,C14_MEN_CS5,P14_RP_LOC,C14_PMEN,P14_POP65P,P14_RP_ACHTOT,C14_POP15P_CS8,C14_PMEN_MENCOUPSENF,P14_POP0205,C14_ACTOCC1564_CS3,DEC_D314,P14_ACTOCC15P_TP,P14_RPAPPART_ACH90,P14_ACTOCC15P_ILT4,P14_RPMAISON_ACH11,P14_POP15P,C14_PMEN_MENCOUPAENF,DEC_D714,DEC_Q314,P14_NSCOL15P,C14_MEN_CS3,C14_ACTOCC15P_VOIT,P14_RPAPPART_ACH45,DEC_PTSA14,P14_RP_1P,P14_CHOM2554,C14_MENSFAM,P14_RP_CCIND,P14_SAL15P,C14_MEN_CS8,P14_RP_4060M2,P14_RPAPPART_ACH11,P14_RP_80100M2,C14_PMEN_MENSFAM,P14_NBPI_RP_ANEM0204,P14_RP_5PP,NB_B302
3,P14_POPMEN2554,P14_RP_6080M2,P14_ACTOCC15P_ILT3,P14_PMEN,P14_NSAL15P_INDEP,P14_RPAPPART_ACH70,P14_SCOL0205,P14_NSAL15P_TP,P14_RP_3040M2,C14_ACT1564_CS6,P14_NBPI_RP_ANEM10P,P14_ANEM_RP,P14_RP_3P,P14_POP0610,P14_NSAL15P,AREA,P14_POP1824,P14_MEN_ANEM0002,P14_NSCOL15P_SUP,DENSITY,C14_MENPSEUL,P14_POPMEN1524,P14_POP0014,P14_POP2539,C14_ACT1564,P14_POPMEN5579,P14_RPMAISON,P14_POP,C14_MENCOUPSENF,DEC_D614,P14_MEN_ANEM0204,P14_ANEM_RP_LOCHLMV,P14_RP_LOCHLMV,P14_LOGVAC,DEC_D814,DEC_D914,P14_ACTOCC1524,P14_NPER_RP_PROP,P14_POP0305,P14_NSCOL15P_DIPLMIN,C14_ACTOCC15P_MAR,P14_APPART,P14_ACTOCC2554,P14_NPER_RP_LOCHLMV,P14_RP_VOIT1,C14_POP15P_CS6,P14_RPAPPART_ACH19,P14_POP1517,P14_SAL15P_EMPAID,P14_POP2529,P14_POP15P_MARIEE,DEC_D414,P14_POP1117,P14_RP_CCCOLL,C14_PMEN_CS3,P14_RP_ACH19,P14_CHOM1524,P14_ANEM_RP_PROP,P14_SCOL2529,P14_ACTOCC15P_ILT2,P14_MAISON,C14_MEN_CS2,P14_PMEN_ANEM0509,P14_RP_ACH70,P14_RP_VOIT1P,P14_RP_2P,P14_POP6579,P14_SAL15P_CDI,P14_ACTOCC15P_ILT2P,P14_RP_M30M2,P14_AINACT1564,C14_PMEN_MENFAMMONO,P14_ACTOCC5564,P14_NPER_RP_GRAT,C14_ACTOCC1564_CS6,P14_POP1524,P14_RPAPPART_ACHTOT,P14_PMEN_ANEM10P,C14_ACTOCC15P_DROU,C14_PMEN_MENFAM,P14_RP_ACH90,C14_PMEN_CS6,C14_MENFSEUL,C14_NE24F2,C14_ACT1564_CS3,P14_NSCOL15P_BAC,P14_ACTOCC15P_ILT1,P14_POP_IMM,P14_RSECOCC,C14_MENFAMMONO,P14_RP_CINDELEC,P14_ACTOCC1564,P14_RP_SDB,P14_SCOL0610,P14_NBPI_RPMAISON,P14_POP1524_PSEUL,P14_POP5579,P14_NPER_RP_LOC,C14_PMEN_CS8,C14_ACTOCC15P,DEC_MED14,P14_ACT1524,P14_RPMAISON_ACHTOT,P14_RPAPPART,P14_RPMAISON_ACH90,P14_NBPI_RPAPPART,C14_COUPAENF,P14_POP_ETR,P14_ACT5564,C14_POP15P_CS5,P14_ANEM_RP_GRAT,P14_NBPI_RP_ANEM0002,C14_MENCOUPAENF,C14_ACTOCC1564_CS5,P14_ANEM_RP_LOC,C14_ACTOCC15P_PAS,P14_RPAPPART_ACH05,P14_ACTOCC15P,C14_ACTOCC15P_TCOM,P14_ACT1564,P14_SCOL30P,C14_PMEN,P14_NSCOL15P_CAPBEP,C14_PMEN_MENCOUPSENF,C14_POP15P_CS8,P14_POP0205,DEC_D314,P14_RPAPPART_ACH90,P14_SAL15P_APPR,C14_PMEN_MENCOUPAENF,DEC_D714,DEC_Q314,C14_MEN_CS3,P14_NSCOL15P,C14_ACTOCC15P_VOIT,P14_PMEN_ANEM0204,P14_RPAPPART_ACH45,P14_RP_1P,P14_RP_CCIND,P14_SAL15P,P14_RP_4060M2,P14_RPMAISON_ACH45,C14_ACTOCC1564_CS2,C14_POP15P_CS3,C14_PMEN_MENSFAM,C14_ACT1564_CS2,P14_NBPI_RP_ANEM0204,P14_RP_5PP,P14_SCOL1517
4,P14_RP_6080M2,P14_PMEN_ANEM0002,P14_MEN_ANEM10P,P14_ACTOCC15P_ILT3,P14_PMEN,DEC_Q114,P14_RPAPPART_ACH70,P14_SCOL0205,P14_RP_3040M2,P14_NBPI_RP_ANEM10P,P14_RP_3P,P14_POP0610,AREA,P14_MEN_ANEM0002,P14_NSCOL15P_SUP,P14_POP6074,DENSITY,P14_POP0014,P14_POP2539,P14_POPMEN5579,C14_NE24F4P,P14_RPMAISON,P14_POP,P14_CHOM5564,P14_SCOL1824,DEC_D614,P14_POP1564,P14_ANEM_RP_LOCHLMV,P14_RP_LOCHLMV,P14_LOGVAC,C14_ACTOCC1564,DEC_D814,DEC_D914,P14_NPER_RP_PROP,P14_POP0305,P14_NSCOL15P_DIPLMIN,C14_ACTOCC15P_MAR,P14_APPART,P14_RP_ACH05,DEC_D114,P14_ACTOCC2554,P14_NPER_RP_LOCHLMV,P14_RP_VOIT1,P14_POP15P_MARIEE,P14_RPAPPART_ACH19,DEC_D414,P14_ACTOCC15P_ILT5,P14_RP_CCCOLL,C14_PMEN_CS3,P14_RP_ACH19,P14_INACT1564,P14_ANEM_RP_PROP,P14_PHORMEN,P14_SCOL2529,P14_RP_2P,P14_ACTOCC15P_ILT2,P14_PMEN_ANEM0509,P14_RP_ACH70,P14_POP6579,P14_POP2554,P14_ACTOCC15P_ILT2P,P14_POP75P,P14_RP_M30M2,P14_ACTOCC5564,P14_POP15P_NONMARIEE,P14_NPER_RP_GRAT,P14_RPAPPART_ACHTOT,P14_POP1524,P14_PMEN_ANEM10P,C14_PMEN_MENFAM,C14_ACTOCC15P_DROU,P14_RP_ACH90,C14_PMEN_CS6,C14_POP15P_CS7,C14_ACT1564_CS3,P14_RP_ACH11,DEC_D214,P14_NSCOL15P_BAC,P14_ACTOCC15P_ILT1,P14_POP_IMM,P14_RSECOCC,P14_RP_CINDELEC,C14_NE24F1,P14_ACTOCC1564,P14_POP4559,C14_PMEN_CS5,P14_NBPI_RPMAISON,P14_POP1524_PSEUL,P14_RP_ACH45,P14_POP5579,P14_NPER_RP_LOC,C14_PMEN_CS8,P14_RP_120M2P,DEC_MED14,C14_MEN_CS6,P14_RPAPPART,P14_RPMAISON_ACH90,P14_NBPI_RPAPPART,P14_POP2064,P14_POP5564,P14_POP_ETR,P14_ACT5564,P14_ANEM_RP_GRAT,P14_NBPI_RP_ANEM0002,P14_ANEM_RP_LOC,P14_RPAPPART_ACH05,C14_ACTOCC15P_TCOM,P14_RP_GARL,C14_POP15P_CS4,P14_RP_LOC,P14_POP0205,C14_ACTOCC1564_CS3,P14_RPAPPART_ACH90,DEC_D714,P14_RP_100120M2,DEC_Q314,C14_MEN_CS3,P14_RP_GRAT,P14_RPAPPART_ACH45,P14_RP_1P,C14_MENSFAM,C14_MEN_CS8,P14_RP_4060M2,P14_RPAPPART_ACH11,P14_RPMAISON_ACH45,C14_POP15P_CS3,P14_RP_80100M2,P14_ETUD1564,C14_PMEN_MENSFAM,P14_NBPI_RP_ANEM0204,P14_RP_5PP,,,,,,,,,,,,,,,,,,
5,C14_POP15P,P14_RP_6080M2,P14_PMEN_ANEM0002,P14_MEN_ANEM10P,P14_ACTOCC15P_ILT3,DEC_Q114,P14_RPAPPART_ACH70,P14_RP_3040M2,C14_ACT1564_CS6,P14_NBPI_RP_ANEM10P,P14_ANEM_RP,P14_RP_3P,P14_NSAL15P,AREA,P14_POP1824,P14_MEN_ANEM0002,P14_NSCOL15P_SUP,DENSITY,C14_ACT1564,P14_POPMEN1524,C14_MENPSEUL,P14_POP2539,P14_POP,P14_NSAL15P_EMPLOY,P14_SCOL1824,DEC_D614,P14_MEN_ANEM0204,P14_ANEM_RP_LOCHLMV,P14_RP_LOCHLMV,P14_LOGVAC,P14_POP4054,DEC_D814,DEC_D914,P14_RP_4P,P14_NPER_RP_PROP,P14_NSCOL15P_DIPLMIN,C14_ACTOCC15P_MAR,P14_APPART,P14_POP1529,P14_RP_ACH05,DEC_D114,P14_NPER_RP_LOCHLMV,P14_RP_VOIT1,C14_MENHSEUL,P14_RPMAISON_ACH19,P14_POP15P_MARIEE,P14_RPAPPART_ACH19,DEC_D414,P14_RP_CCCOLL,C14_PMEN_CS3,P14_RP_ACH19,P14_CHOM1524,C14_PMEN_CS7,P14_ANEM_RP_PROP,P14_PHORMEN,P14_SCOL2529,P14_RP_2P,P14_ACTOCC15P_ILT2,C14_MEN_CS2,P14_RP_ACH70,P14_RP_VOIT1P,P14_ACTOCC15P_ILT2P,P14_RP,P14_POP75P,P14_RP_M30M2,C14_PMEN_MENFAMMONO,P14_POP15P_NONMARIEE,C14_ACTOCC1564_CS6,P14_POPMEN15P,P14_POP_FR,P14_PMEN_ANEM10P,C14_PMEN_MENFAM,P14_RP_ACH90,P14_NBPI_RP,C14_PMEN_CS6,C14_MENFSEUL,C14_ACT1564_CS3,DEC_D214,P14_ACTOCC15P_ILT1,P14_POP_IMM,P14_RSECOCC,P14_RP_CINDELEC,P14_POP4559,C14_PMEN_CS5,P14_POP1524_PSEUL,P14_RP_ACH45,P14_POP5579,P14_NPER_RP_LOC,P14_RPMAISON_ACH70,C14_PMEN_CS8,C14_PMEN_CS4,C14_ACTOCC15P,DEC_MED14,C14_MEN_CS6,P14_ACT1524,P14_RPAPPART,P14_RPMAISON_ACH90,P14_NBPI_RPAPPART,P14_POP_ETR,P14_ANEM_RP_GRAT,P14_NBPI_RP_ANEM0002,C14_ACTOCC1564_CS5,P14_ANEM_RP_LOC,P14_RPAPPART_ACH05,P14_ACTOCC15P,C14_ACTOCC15P_TCOM,P14_RP_GARL,P14_ACT1564,P14_SCOL30P,P14_POP0019,P14_RP_LOC,C14_PMEN,P14_NSCOL15P_CAPBEP,P14_RP_ACHTOT,C14_POP15P_CS8,P14_POP0205,C14_ACTOCC1564_CS3,DEC_D314,P14_RPAPPART_ACH90,P14_SAL15P_APPR,DEC_D714,P14_RP_100120M2,C14_MEN_CS3,P14_NSCOL15P,DEC_Q314,C14_ACTOCC15P_VOIT,P14_RP_GRAT,P14_RPAPPART_ACH45,P14_RP_1P,P14_RPMAISON_ACH05,C14_MENSFAM,C14_MEN_CS8,P14_RP_4060M2,P14_RPAPPART_ACH11,P14_RP_80100M2,C14_POP15P_CS3,C14_PMEN_MENSFAM,P14_NBPI_RP_ANEM0204,NB_B302,,,,,,,,,,
predihood/generated_files/selected-indicators/list20.csv deleted 100644 → 0
+ 0
− 6
View file @ 3911efd3
0,C14_PMEN_MENCOUPSENF,P14_POP0205,P14_RPAPPART_ACH19,C14_ACTOCC1564_CS3,DEC_D414,P14_NBPI_RPMAISON,C14_PMEN_CS3,P14_RP_ACH19,P14_ANEM_RP_PROP,P14_ACTOCC15P_ILT2,P14_MAISON,P14_PMEN_ANEM0509,C14_PMEN_MENCOUPAENF,AREA,P14_POP1824,P14_ACTOCC15P_ILT2P,DENSITY,P14_RPMAISON_ACHTOT,P14_RPAPPART,P14_NBPI_RPAPPART,P14_RPMAISON,P14_POP,C14_NE24F3,P14_SCOL1824,P14_RP_80100M2,C14_POP15P_CS3,P14_RPMAISON_ACH45,C14_ACTOCC15P_DROU,C14_PMEN_MENFAM,P14_RP_ACH90,C14_ACT1564_CS3,C14_ACTOCC15P_MAR,P14_RP_5PP,P14_SCOL1517,,
1,C14_ACTOCC1564_CS3,P14_RP_3040M2,C14_PMEN_CS3,P14_POP1524_PSEUL,P14_RPAPPART_ACH90,P14_RP_CCCOLL,P14_RP_ACH45,P14_ACTOCC15P_ILT2,NB_A504,P14_PMEN_ANEM0509,P14_NPER_RP_LOC,P14_POP0610,AREA,C14_MEN_CS6,DENSITY,P14_RP_PROP,P14_RPMAISON_ACHTOT,P14_POP,C14_FAMMONO,C14_POP15P_CS3,P14_ANEM_RP_LOCHLMV,P14_RP_LOCHLMV,P14_RP_ACH90,C14_PMEN_CS6,P14_NPER_RP_PROP,C14_ACT1564_CS3,C14_PMEN_MENSFAM,P14_POP0019,P14_APPART,P14_POP_IMM,,,,,,
2,C14_ACTOCC1564_CS3,DEC_Q114,P14_RPAPPART_ACH70,DEC_D414,C14_PMEN_CS7,P14_ANEM_RP,P14_POP1524_PSEUL,P14_ANEM_RP_PROP,P14_POP15P,P14_ACTOCC15P_ILT2,P14_RP_ACH45,P14_POP0610,AREA,DEC_Q314,P14_NSCOL15P,P14_ACTOCC15P_ILT2P,DENSITY,P14_POPMEN5579,P14_RP_VOIT2P,P14_RPMAISON_ACH90,P14_NBPI_RPAPPART,C14_MENSFAM,P14_POP,P14_SCOL1824,DEC_D614,C14_PMEN_MENFAM,DEC_D814,C14_NE24F2,P14_NPER_RP_PROP,C14_PMEN_MENSFAM,P14_APPART,DEC_D114,,,,
3,P14_RP_VOIT1,C14_PMEN_MENCOUPSENF,P14_RPAPPART_ACH19,P14_RPAPPART_ACH70,DEC_D414,P14_NBPI_RPMAISON,C14_PMEN_CS3,P14_RP_ACH19,P14_ANEM_RP_PROP,P14_ACTOCC15P_ILT2,P14_MAISON,P14_POP0610,AREA,P14_POP1824,P14_MEN_ANEM0002,P14_ACTOCC15P_ILT2P,P14_NSCOL15P_SUP,DENSITY,P14_RP_M30M2,P14_RP_1P,P14_ACT1524,C14_PMEN_MENFAMMONO,P14_RPMAISON_ACH90,P14_POP,P14_ANEM_RP_LOCHLMV,P14_LOGVAC,C14_PMEN_MENFAM,P14_RP_ACH90,C14_PMEN_CS6,P14_NPER_RP_PROP,C14_ACTOCC15P_TCOM,C14_ACT1564_CS2,C14_ACTOCC15P_MAR,P14_APPART,P14_ACTOCC15P_ILT1,P14_POP_IMM
4,P14_NPER_RP_LOCHLMV,P14_RP_CINDELEC,P14_ACTOCC15P_ILT3,P14_RPAPPART_ACH19,P14_RPAPPART_ACH70,P14_RP_CCCOLL,P14_RPAPPART_ACH90,P14_POP1524_PSEUL,P14_RP_ACH19,P14_RP_ACH45,P14_RP_ACH70,AREA,DENSITY,P14_RP_M30M2,P14_POP,C14_MEN_CS8,P14_POP_ETR,P14_RPAPPART_ACH11,C14_POP15P_CS3,P14_ANEM_RP_LOCHLMV,P14_NBPI_RP_ANEM0002,P14_RP_LOCHLMV,P14_ANEM_RP_LOC,P14_RPAPPART_ACH05,C14_ACTOCC15P_TCOM,C14_ACTOCC15P_MAR,P14_RP_LOC,DEC_D114,,,,,,,,
5,C14_MENHSEUL,P14_RP_CINDELEC,P14_ACTOCC15P_ILT3,P14_RPAPPART_ACH19,DEC_Q114,DEC_D314,P14_RP_CCCOLL,C14_PMEN_CS3,P14_RP_ACH19,P14_RP_2P,P14_NPER_RP_LOC,AREA,DEC_D714,C14_MEN_CS3,DEC_Q314,P14_ACTOCC15P_ILT2P,DEC_MED14,C14_ACTOCC15P_VOIT,DENSITY,P14_RP_M30M2,P14_RP_1P,P14_ACT1524,P14_POP,C14_MEN_CS8,C14_POP15P_CS3,P14_ANEM_RP_GRAT,P14_ANEM_RP_LOCHLMV,DEC_D814,DEC_D914,C14_PMEN_MENSFAM,P14_POP1529,P14_ACTOCC15P_ILT1,P14_POP_IMM,,,
predihood/generated_files/selected-indicators/list30.csv deleted 100644 → 0
+ 0
− 6
View file @ 3911efd3
0,P14_NPER_RP_LOCHLMV,C14_PMEN_MENCOUPSENF,P14_POP0205,P14_RPAPPART_ACH19,C14_ACTOCC1564_CS3,DEC_D414,C14_PMEN_CS5,P14_NBPI_RPMAISON,C14_PMEN_CS3,P14_RP_ACH19,P14_RP_CCCOLL,P14_ANEM_RP,P14_ANEM_RP_PROP,P14_ACTOCC15P_ILT2,P14_MAISON,P14_PMEN_ANEM0509,C14_PMEN_MENCOUPAENF,AREA,P14_POP1824,P14_ACTOCC15P_ILT2P,DENSITY,P14_ACT1524,C14_PMEN_CS2,P14_RPAPPART,P14_RPMAISON_ACHTOT,P14_NBPI_RPAPPART,P14_RPMAISON,P14_POP,P14_RP_CCIND,C14_NE24F3,P14_SCOL1824,DEC_D614,P14_RP_80100M2,C14_POP15P_CS3,P14_RPMAISON_ACH45,P14_ANEM_RP_LOCHLMV,C14_ACTOCC15P_DROU,C14_PMEN_MENFAM,P14_RP_ACH90,DEC_D914,C14_MENFSEUL,C14_NE24F2,P14_RP_GARL,C14_ACT1564_CS3,C14_ACTOCC15P_MAR,P14_RP_5PP,P14_SCOL1517,C14_PMEN,,,
1,P14_RSECOCC,C14_POP15P_CS8,C14_ACTOCC1564_CS3,P14_POP4559,DEC_D414,P14_RP_3040M2,C14_PMEN_CS3,P14_POP1524_PSEUL,P14_RPAPPART_ACH90,P14_RP_CCCOLL,P14_RP_ACH45,P14_ACTOCC15P_ILT2,NB_A504,P14_RP_3P,P14_PMEN_ANEM0509,P14_NPER_RP_LOC,P14_POP0610,AREA,C14_PMEN_MENCOUPAENF,P14_NBPI_RP_ANEM0509,C14_MEN_CS6,DENSITY,P14_AINACT1564,P14_RP_PROP,P14_RPAPPART,P14_RPMAISON_ACHTOT,P14_POPMEN5579,C14_MENSFAM,P14_POP,C14_FAMMONO,P14_RP_4060M2,C14_POP15P_CS3,P14_ANEM_RP_LOCHLMV,P14_RP_LOCHLMV,C14_ACTOCC1564,P14_POP4054,P14_RP_ACH90,P14_ANEM_RP_LOC,C14_PMEN_CS6,C14_ACTOCC15P_PAS,P14_NPER_RP_PROP,C14_ACT1564_CS3,C14_PMEN_MENSFAM,P14_POP0019,P14_APPART,DEC_D214,P14_RP_ACH05,P14_POP_IMM,,,
2,P14_NPER_RP_LOCHLMV,P14_PMEN_ANEM0002,C14_POP15P_CS8,P14_POP15P_MARIEE,P14_PMEN,C14_ACTOCC1564_CS3,DEC_Q114,P14_RPAPPART_ACH70,DEC_D414,C14_PMEN_CS7,C14_PMEN_CS3,P14_POP1524_PSEUL,P14_ANEM_RP,P14_ANEM_RP_PROP,P14_POP15P,P14_ACTOCC15P_ILT2,NB_A504,P14_RP_ACH45,P14_PMEN_ANEM0509,P14_POP0610,AREA,DEC_Q314,P14_NSCOL15P,P14_ACTOCC15P_ILT2P,P14_NSCOL15P_SUP,DENSITY,P14_RP_M30M2,P14_RPAPPART_ACH45,P14_RP_1P,P14_POPMEN5579,P14_RP_VOIT2P,P14_RP_PROP,P14_RPMAISON_ACH90,P14_NBPI_RPAPPART,C14_MENSFAM,P14_POP,P14_SAL15P,P14_SCOL1824,DEC_D614,P14_ANEM_RP_LOCHLMV,C14_PMEN_MENFAM,DEC_D814,DEC_D914,C14_NE24F2,P14_NPER_RP_PROP,C14_PMEN_MENSFAM,P14_APPART,DEC_D214,DEC_D114,,
3,P14_RP_VOIT1,C14_PMEN_MENCOUPSENF,P14_ACTOCC15P_ILT3,P14_RPAPPART_ACH19,P14_PMEN,P14_RPAPPART_ACH70,DEC_D414,P14_RP_CCCOLL,C14_PMEN_CS3,P14_RP_ACH19,P14_RP_3040M2,P14_NBPI_RPMAISON,P14_ANEM_RP_PROP,P14_ANEM_RP,P14_ACTOCC15P_ILT2,P14_MAISON,P14_PMEN_ANEM0509,P14_POP0610,P14_RP_ACH70,AREA,P14_POP1824,P14_MEN_ANEM0002,C14_ACTOCC15P,P14_ACTOCC15P_ILT2P,P14_NSCOL15P_SUP,P14_PMEN_ANEM0204,DENSITY,P14_RP_M30M2,P14_POPMEN1524,P14_RP_1P,P14_ACT1524,C14_PMEN_MENFAMMONO,P14_RPMAISON_ACHTOT,P14_RPMAISON_ACH90,P14_POP,P14_RP_4060M2,P14_POP_ETR,C14_ACTOCC1564_CS2,P14_ANEM_RP_LOCHLMV,P14_LOGVAC,C14_PMEN_MENFAM,P14_RP_ACH90,C14_PMEN_CS6,P14_NPER_RP_PROP,C14_ACTOCC15P_TCOM,C14_ACT1564_CS2,C14_ACTOCC15P_MAR,P14_APPART,P14_NSCOL15P_BAC,P14_ACTOCC15P_ILT1,P14_POP_IMM
4,P14_NPER_RP_LOCHLMV,P14_RP_CINDELEC,P14_ACTOCC15P_ILT3,P14_RPAPPART_ACH19,P14_RPAPPART_ACH70,P14_SCOL0205,P14_RP_CCCOLL,C14_PMEN_CS3,P14_POP1524_PSEUL,P14_RP_ACH19,P14_RPAPPART_ACH90,P14_ANEM_RP_PROP,P14_RP_ACH45,P14_ACTOCC15P_ILT2,P14_RP_ACH70,P14_NPER_RP_LOC,AREA,C14_PMEN_CS8,DENSITY,P14_RP_M30M2,P14_APPART,P14_RP_1P,P14_POP,C14_MEN_CS8,P14_POP_ETR,P14_RPAPPART_ACH11,DEC_D614,C14_POP15P_CS3,DEC_D114,P14_ANEM_RP_LOCHLMV,P14_NBPI_RP_ANEM0002,P14_LOGVAC,P14_RP_LOCHLMV,P14_ANEM_RP_LOC,P14_RP_ACH90,DEC_D814,P14_RPAPPART_ACH05,C14_ACTOCC15P_TCOM,P14_RP_GARL,C14_ACTOCC15P_MAR,P14_RP_LOC,P14_RP_ACH11,P14_ACTOCC15P_ILT1,,,,,,,,
5,P14_RSECOCC,C14_MENHSEUL,P14_RP_CINDELEC,P14_ACTOCC15P_ILT3,P14_RPAPPART_ACH19,DEC_Q114,C14_ACTOCC1564_CS3,DEC_D314,DEC_D414,P14_RP_CCCOLL,C14_PMEN_CS3,P14_RP_ACH19,P14_RP_ACH45,P14_RP_2P,P14_NPER_RP_LOC,AREA,DEC_D714,C14_MEN_CS3,DEC_Q314,P14_ACTOCC15P_ILT2P,DEC_MED14,C14_ACTOCC15P_VOIT,P14_RP,DENSITY,P14_RP_M30M2,P14_RPAPPART_ACH45,P14_RP_1P,P14_ACT1524,P14_POP,C14_MEN_CS8,P14_POP_ETR,DEC_D114,DEC_D614,C14_POP15P_CS3,P14_ANEM_RP_GRAT,P14_ANEM_RP_LOCHLMV,P14_NBPI_RP_ANEM0002,P14_LOGVAC,C14_PMEN_CS6,DEC_D814,DEC_D914,P14_RPAPPART_ACH05,C14_PMEN_MENSFAM,P14_SCOL30P,P14_POP1529,P14_ACTOCC15P_ILT1,P14_POP_IMM,,,,
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