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Commit 50fd07f6 authored by Guillaume Duret's avatar Guillaume Duret
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reading statistics to build chart

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with 101 additions and 29 deletions
read_count.py
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......@@ -89,8 +89,7 @@ if __name__ == '__main__':
dataset_type = data_options[choice]
dataset_name = f"/gpfsscratch/rech/uli/ubn15wo/dataset/s2rg/Fruits_all_medium/GUIMOD_{choice}"
list_categories = [ "apple2" , "apricot", "banana1", "kiwi1", "lemon2", "orange2", "peach1", "pear2"]
#list_Nb_instance = [ "apple2" , "apricot", "banana1", "kiwi1", "lemon2", "orange2", "peach1", "pear2"]
path_json = "Count_150000.json"
......@@ -119,15 +118,6 @@ if __name__ == '__main__':
mix_eval=[]
mix_test=[]
array_apple=[]
array_apricot=[]
array_banana=[]
array_kiwi=[]
array_lemon=[]
array_orange=[]
array_peach=[]
array_pear=[]
stat_cat_inst = {}
for scenario_loop in scenarios :
......@@ -135,12 +125,10 @@ if __name__ == '__main__':
for cat in list_categories :
stat_cat_inst[scenario_loop][cat] = {}
for cat in list_categories :
stat_cat[cat] = {}
for scenario_loop in scenarios :
stat_cat[cat][scenario_loop] = {}
for destination_folder_loop in destination_folders_list[scenario_loop] : # [f"Generated_{scenario}_Testing", f"Generated_{scenario}_Evaluating", f"Generated_{scenario}_Training"] :
#print("scenario_loop : " , scenario_loop)
#print("destination_folder_loop : " , destination_folder_loop)
......@@ -200,24 +188,107 @@ if __name__ == '__main__':
print(mix_eval)
print(mix_test)
list_Nb_instance = [ "0_instances" , "1_instances", "2_instances", "3_instances", "4_instances", "5_instances" ,
"6_instances", "7_instances" , "8_instances", "9_instances","10_instances","11_instances",
"12_instances", "13_instances" , "14_instances", "15_instances","16_instances","17_instances", "18_instances"]
array_apple=[]
array_apricot=[]
array_banana=[]
array_kiwi=[]
array_lemon=[]
array_orange=[]
array_peach=[]
array_pear=[]
# for scenario_loop in scenarios :
# for cat in list_categories :
# for destination_folder_loop in destination_folders_list[scenario_loop] :
# stat_cat_inst[scenario_loop][cat][nb_inst]
df1_train=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_train), np.array(cameras_train), np.array(mix_train)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
df2_eval=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_eval), np.array(cameras_eval), np.array(mix_eval)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
df3_test=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_test), np.array(cameras_test), np.array(mix_test)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
list_categories = [ "apple2" , "apricot", "banana1", "kiwi1", "lemon2", "orange2", "peach1", "pear2"]
df1 = prep_df(df1_train, 'Train')
df2 = prep_df(df2_eval, 'Eval')
df3 = prep_df(df3_test, 'Test')
for cat in list_categories :
for nb_inst in list_Nb_instance :
print(stat_cat_inst[scenario_loop][cat].keys())
if nb_inst in stat_cat_inst[scenario_loop][cat].keys() : #because some rare occurance of large instance number
if cat == "apple2" :
array_apple.append(stat_cat_inst[scenario_loop][cat][nb_inst])
if cat == "apricot" :
array_apricot.append(stat_cat_inst[scenario_loop][cat][nb_inst])
if cat == "banana1" :
array_banana.append(stat_cat_inst[scenario_loop][cat][nb_inst])
if cat == "kiwi1" :
array_kiwi.append(stat_cat_inst[scenario_loop][cat][nb_inst])
if cat == "lemon2" :
array_lemon.append(stat_cat_inst[scenario_loop][cat][nb_inst])
if cat == "orange2" :
array_orange.append(stat_cat_inst[scenario_loop][cat][nb_inst])
if cat == "peach1" :
array_peach.append(stat_cat_inst[scenario_loop][cat][nb_inst])
if cat == "pear2" :
array_pear.append(stat_cat_inst[scenario_loop][cat][nb_inst])
else : #because some rare occurance of large instance number
print(nb_inst)
if cat == "apple2" :
array_apple.append(0)
if cat == "apricot" :
array_apricot.append(0)
if cat == "banana1" :
array_banana.append(0)
if cat == "kiwi1" :
array_kiwi.append(0)
if cat == "lemon2" :
array_lemon.append(0)
if cat == "orange2" :
array_orange.append(0)
if cat == "peach1" :
array_peach.append(0)
if cat == "pear2" :
array_pear.append(0)
print()
print(array_apple)
print(array_apricot)
print(array_banana)
print(array_kiwi)
print(array_lemon)
print(array_orange)
print(array_peach)
print(array_pear)
print(len(array_pear))
print(np.resize( array_apple , (3, 19)))
df_apple=pd.DataFrame(np.resize( array_apple , (1, 19)),index=["Dataset"],columns=list_Nb_instance)
df_apricot=pd.DataFrame(np.resize( array_apricot , (1, 19)),index=["Dataset"],columns=list_Nb_instance)
df_banana=pd.DataFrame(np.resize( array_banana , (1, 19)),index=["Dataset"],columns=list_Nb_instance)
df_kiwi=pd.DataFrame(np.resize( array_kiwi , (1, 19)),index=["Dataset"],columns=list_Nb_instance)
df_lemon=pd.DataFrame(np.resize( array_lemon , (1, 19)),index=["Dataset"],columns=list_Nb_instance)
df_orange=pd.DataFrame(np.resize( array_orange , (1, 19)),index=["Dataset"],columns=list_Nb_instance)
df_peach=pd.DataFrame(np.resize( array_peach , (1, 19)),index=["Dataset"],columns=list_Nb_instance)
df_pear=pd.DataFrame(np.resize( array_pear , (1, 19)),index=["Dataset"],columns=list_Nb_instance)
df_apple1 = prep_df(df_apple, 'Apple')
df_apricot1 = prep_df(df_apricot, 'Apricot')
df_banana1 = prep_df(df_banana, 'Banana')
df_kiwi1 = prep_df(df_kiwi, 'Kiwi')
df_lemon1 = prep_df(df_lemon, 'Lemon')
df_orange1 = prep_df(df_orange, 'Orange')
df_peach1 = prep_df(df_peach, 'Peach')
df_pear1 = prep_df(df_pear, 'Pear')
df = pd.concat([df_apple1, df_apricot1, df_banana1, df_kiwi1, df_lemon1, df_orange1, df_peach1, df_pear1])
# qxqxs
# df1_train=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_train), np.array(cameras_train), np.array(mix_train)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
# df2_eval=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_eval), np.array(cameras_eval), np.array(mix_eval)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
# df3_test=pd.DataFrame(np.resize(np.concatenate((np.array(worlds_test), np.array(cameras_test), np.array(mix_test)) ), (3,8)),index=["World", "Cameras", "All"],columns=[ "apple" , "apricot", "banana", "kiwi", "lemon", "orange", "peach", "pear"])
# df1 = prep_df(df1_train, 'Train')
# df2 = prep_df(df2_eval, 'Eval')
# df3 = prep_df(df3_test, 'Test')
df = pd.concat([df1, df2, df3])
# df = pd.concat([df1, df2, df3])
# print(np.resize(np.concatenate((np.array(worlds_train), np.array(cameras_train), np.array(mix_train)) ), (3,8)))
......@@ -242,9 +313,10 @@ if __name__ == '__main__':
alt.renderers.enable('altair_viewer')
chart = alt.Chart(df).mark_bar().encode(
# tell Altair which field to group columns on
x=alt.X('c2:N', title=None),
x=alt.X('c2:N', title=None, sort=None),
# tell Altair which field to use as Y values and how to calculate
y=alt.Y('sum(values):Q',
......
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