diff --git a/scripts/quads.py b/scripts/quads.py
index 0897c667e1d60befd2a7e17edd473656b9038144..fb75e3beeaf26916d27e7626f689bb3ea91624d9 100644
--- a/scripts/quads.py
+++ b/scripts/quads.py
@@ -43,21 +43,21 @@ def bi(n,k) :
"""
return math.comb(n,k)
-def sdquali(df, columns, vchi2, threshold_chi2, threshold_fisher_exact) :
+def sdquali(df, columns, variable_cat, threshold_chi2, threshold_fisher_exact) :
"""
Function used to select which modalities are over and under represented
- in the different groups of the vchi2
+ in the different groups of the variable_cat
Actions performed:
- * Chi2 test for the variables with the vchi2
- * Separate the pandas a dictionary of pandas for each group of vchi2
+ * Chi2 test for the variables with the variable_cat
+ * Separate the pandas a dictionary of pandas for each group of variable_cat
* Make the v-test and final statistics
Args:
df: a pandas DataFrame
columns : the selected columns
- vchi2 : the variable to test the chi2
+ variable_cat : the variable to test the chi2
chi2_p_value : p-value for the chi2 test
Returns:
@@ -65,7 +65,7 @@ def sdquali(df, columns, vchi2, threshold_chi2, threshold_fisher_exact) :
for the variables
"""
- #each columns make a chi2 test with the variable vchi2
+ #each columns make a chi2 test with the variable variable_cat
global column
column = []
chi_p_value = []
@@ -78,7 +78,7 @@ def sdquali(df, columns, vchi2, threshold_chi2, threshold_fisher_exact) :
chi_significative = []
fisher_significative = []
for col in df[columns]:
- cont = pd.crosstab(df[col],df[vchi2])
+ cont = pd.crosstab(df[col],df[variable_cat])
cat_modalities = cont.columns.tolist()
# Chi-square test of independence
chi2, p_chi2, dof, expected = chi2_contingency(cont)
@@ -131,7 +131,7 @@ def sdquali(df, columns, vchi2, threshold_chi2, threshold_fisher_exact) :
count_pvalue = 0
global new_df
new_df = df[column]
- new_df.insert(len(column),vchi2,df[vchi2].to_list())
+ new_df.insert(len(column),variable_cat,df[variable_cat].to_list())
#generate the table from the chi2 test with the variables and their p_value
X2 = pd.DataFrame({'Variables' : chi_column,
@@ -145,31 +145,31 @@ def sdquali(df, columns, vchi2, threshold_chi2, threshold_fisher_exact) :
'interpretation' : fisher_significative})
return X2, FISHER
-def quali_analysis(vchi2):
+def quali_analysis(variable_cat):
"""
Function used to select which modalities are over and under represented
- in the different groups of the vchi2
+ in the different groups of the variable_cat
Actions performed:
- * Chi2 test for the variables with the vchi2
- * Separate the pandas a dictionary of pandas for each group of vchi2
+ * Chi2 test for the variables with the variable_cat
+ * Separate the pandas a dictionary of pandas for each group of variable_cat
* Make the v-test and final statistics
Args:
- vchi2 : the variable to test the chi2
+ variable_cat : the variable to test the chi2
Returns:
DataFrame: a pandas DataFrame containing statistics analysis for each
- vchi2, variables and modalities
+ variable_cat, variables and modalities
"""
#separation of the dataframe for each variable chi2 with
#a dictionary of variable chi2
global dictio
dictio = {}
- for i in new_df[vchi2].unique():
- dictio[i] = new_df[ new_df[vchi2] == i ]
- #order the dictionary by the number of vchi2
+ for i in new_df[variable_cat].unique():
+ dictio[i] = new_df[ new_df[variable_cat] == i ]
+ #order the dictionary by the number of variable_cat
dictio = OrderedDict(sorted(dictio.items(), key=lambda x: x[0]))
#column : modalities
@@ -180,13 +180,13 @@ def quali_analysis(vchi2):
unique_elements = new_df[col].unique()
nb_mod_by_var.append(len(unique_elements))
index.extend(unique_elements)
- index_chi2 = np.sort(new_df[vchi2].unique())
+ index_chi2 = np.sort(new_df[variable_cat].unique())
index = [elem for elem in index if elem not in index_chi2]
modality = index * len(index_chi2) + list(index_chi2)
- #column : vchi2
+ #column : variable_cat
global chi2_var
- el = np.sort(new_df[vchi2].unique())
+ el = np.sort(new_df[variable_cat].unique())
variable = [str(element) for element in el for _ in range(len(index))]
chi2_var = variable + list(index_chi2)
@@ -194,15 +194,15 @@ def quali_analysis(vchi2):
global variables
global variables_2
var = [([i]*j) for i,j in zip(column,nb_mod_by_var)]
- nb_vchi2 = [vchi2] * len(new_df[vchi2].unique())
+ nb_variable_cat = [variable_cat] * len(new_df[variable_cat].unique())
variables = []
for i in var:
variables.extend(i)
- variables = variables * len(new_df[vchi2].unique()) + nb_vchi2
+ variables = variables * len(new_df[variable_cat].unique()) + nb_variable_cat
variables_2 = []
for i in var:
variables_2.extend(i)
- variables_2.extend(nb_vchi2)
+ variables_2.extend(nb_variable_cat)
global NA
NA = []
@@ -210,7 +210,7 @@ def quali_analysis(vchi2):
NA.append('Not present')
result = pd.DataFrame({
- vchi2 : chi2_var,
+ variable_cat : chi2_var,
'variables' : variables ,
'modalities':modality,
'cla/mod' : NA,
@@ -222,24 +222,24 @@ def quali_analysis(vchi2):
return result
#column : cla/mod
-def clamod(result, vchi2):
+def clamod(result, variable_cat):
"""
Function used to make the cla/mod column for the result table
Actions performed:
- * create the cla/mod statistics for each vchi2, variables and modalities
+ * create the cla/mod statistics for each variable_cat, variables and modalities
Args:
result : the pandas realised in the sdquali's function
- vchi2 : the variable to test the chi2
+ variable_cat : the variable to test the chi2
Returns:
DataFrame: a pandas DataFrame containing cla/mod statistics analysis
- for each vchi2, variables and modalities
+ for each variable_cat, variables and modalities
"""
#add the cla/mod column from clamod to cla/mod column from result
- clamod = pd.DataFrame(columns = [vchi2,'variables','modalities','cla/mod'])
+ clamod = pd.DataFrame(columns = [variable_cat,'variables','modalities','cla/mod'])
i_clamod = []
l_clamod = []
variable = []
@@ -261,7 +261,7 @@ def clamod(result, vchi2):
for i in i_clamod :
modalities.extend(i)
for g,h,i,j in zip(var_chi2,variable,modalities,l_clamod) :
- clamod = clamod.append({vchi2 : g,
+ clamod = clamod.append({variable_cat : g,
'variables' : h,
'modalities' : i,
'cla/mod' : j },
@@ -269,13 +269,13 @@ def clamod(result, vchi2):
clamod = clamod.fillna(0)
clamod_dict = {
- (str(row[vchi2]), \
+ (str(row[variable_cat]), \
str(row['variables']), \
str(row['modalities'])): round(row['cla/mod'], 7)
for index, row in clamod.iterrows()
}
for i, row in result.iterrows():
- key = (str(row[vchi2]), \
+ key = (str(row[variable_cat]), \
str(row['variables']), \
str(row['modalities']))
if key in clamod_dict:
@@ -284,23 +284,23 @@ def clamod(result, vchi2):
#column : mod/cla
-def modcla(result,vchi2):
+def modcla(result,variable_cat):
"""
Function used to make the mod/cla column for the result table
Actions performed:
- * create the mod/cla statistics for each vchi2, variables and modalities
+ * create the mod/cla statistics for each variable_cat, variables and modalities
Args:
result : the pandas realised in the sdquali's function
- vchi2 : the variable to test the chi2
+ variable_cat : the variable to test the chi2
Returns:
DataFrame: a pandas DataFrame containing mod/cla statistics analysis
- for each vchi2, variables and modalities
+ for each variable_cat, variables and modalities
"""
- modcla = pd.DataFrame(columns = [vchi2,'variables','modalities','mod/cla'])
+ modcla = pd.DataFrame(columns = [variable_cat,'variables','modalities','mod/cla'])
i_modcla = []
l_modcla = []
variable = []
@@ -323,19 +323,19 @@ def modcla(result,vchi2):
for i in i_modcla :
modalities.extend(i)
for g,h,i,j in zip(var_chi2,variable,modalities,l_modcla) :
- modcla = modcla.append({vchi2 : g,
+ modcla = modcla.append({variable_cat : g,
'variables' : h,
'modalities' : i,
'mod/cla' : j },
ignore_index=True)
modcla_dict = {
- (str(row[vchi2]), \
+ (str(row[variable_cat]), \
str(row['variables']), \
str(row['modalities'])): round(row['mod/cla'], 7)
for index, row in modcla.iterrows()
}
for i, row in result.iterrows():
- key = (str(row[vchi2]), \
+ key = (str(row[variable_cat]), \
str(row['variables']), \
str(row['modalities']))
if key in modcla_dict:
@@ -350,14 +350,14 @@ def globa(result):
Actions performed:
- * create the global statistics for each vchi2, variables and modalities
+ * create the global statistics for each variable_cat, variables and modalities
Args:
result : the pandas realised in the sdquali's function
Returns:
DataFrame: a pandas DataFrame containing global statistics
- analysis for each vchi2, variables and modalities
+ analysis for each variable_cat, variables and modalities
"""
glo = pd.DataFrame(columns = ['variables','modalities','global'])
@@ -395,7 +395,7 @@ def globa(result):
return result
#column : p_value
-def pvalue(result,vchi2):
+def pvalue(result,variable_cat):
"""
Function used to make the p-value column for the result table
@@ -406,10 +406,10 @@ def pvalue(result,vchi2):
Args:
result : the pandas realised in the sdquali's function
- vchi2 : the variable to test the chi2
+ variable_cat : the variable to test the chi2
Returns:
- DataFrame: a pandas DataFrame containing the p-value for each vchi2,
+ DataFrame: a pandas DataFrame containing the p-value for each variable_cat,
variables and modalities
"""
global list_pval
@@ -417,7 +417,7 @@ def pvalue(result,vchi2):
global table
table = pd.DataFrame({
- vchi2 : chi2_var,
+ variable_cat : chi2_var,
'variables' : variables,
'modalities': modality,
'nj' : NA,
@@ -437,10 +437,10 @@ def pvalue(result,vchi2):
table_nj = pd.concat(nj_table, ignore_index=True)
table_nj = table_nj.fillna(0)
- table_nk = pd.DataFrame(columns=[vchi2,'nk'])
+ table_nk = pd.DataFrame(columns=[variable_cat,'nk'])
for v, df in dictio.items():
nk = df.shape[0]
- table_nk = table_nk.append({vchi2: v,'nk':nk}, ignore_index=True)
+ table_nk = table_nk.append({variable_cat: v,'nk':nk}, ignore_index=True)
nkj_data = []
@@ -449,20 +449,20 @@ def pvalue(result,vchi2):
nkj = df[c].value_counts().reset_index()
nkj.columns = ['modalities','nkj']
nkj['variables'] = c
- nkj[vchi2] = v
+ nkj[variable_cat] = v
nkj_data.append(nkj)
table_nkj = pd.concat(nkj_data, ignore_index=True)
table_nkj = table_nkj.fillna(0)
table.merge(table_nj[['variables', 'modalities', 'nj']], \
on=['variables', 'modalities'], how='left')
- table.merge(table_nkj[[vchi2, 'variables', 'modalities', 'nkj']], \
- on=[vchi2, 'variables', 'modalities'], how='left')
- table.merge(table_nk[[vchi2, 'nk']],\
- on=[vchi2], how='left')
+ table.merge(table_nkj[[variable_cat, 'variables', 'modalities', 'nkj']], \
+ on=[variable_cat, 'variables', 'modalities'], how='left')
+ table.merge(table_nk[[variable_cat, 'nk']],\
+ on=[variable_cat], how='left')
table_nkj_dict = {
- (str(row[vchi2]), str(row['variables']), str(row['modalities'])): row['nkj']
+ (str(row[variable_cat]), str(row['variables']), str(row['modalities'])): row['nkj']
for _, row in table_nkj.iterrows()
}
table_nj_dict = {
@@ -470,17 +470,17 @@ def pvalue(result,vchi2):
for _, row in table_nj.iterrows()
}
table_nk_dict = {
- (str(row[vchi2])): row['nk']
+ (str(row[variable_cat])): row['nk']
for _, row in table_nk.iterrows()
}
for i, row in table.iterrows():
- key_nkj = (str(row[vchi2]), str(row['variables']), str(row['modalities']))
+ key_nkj = (str(row[variable_cat]), str(row['variables']), str(row['modalities']))
if key_nkj in table_nkj_dict:
table.at[i, 'nkj'] = table_nkj_dict[key_nkj]
key_nj = (str(row['variables']), str(row['modalities']))
if key_nj in table_nj_dict:
table.at[i, 'nj'] = table_nj_dict[key_nj]
- key_nk = str(row[vchi2])
+ key_nk = str(row[variable_cat])
if key_nk in table_nk_dict:
table.at[i, 'nk'] = table_nk_dict[key_nk]
if table.at[i, 'nkj'] == 'Not present':
@@ -488,7 +488,7 @@ def pvalue(result,vchi2):
table_dict = {
- (str(row[vchi2]), str(row['variables']), str(row['modalities'])): {
+ (str(row[variable_cat]), str(row['variables']), str(row['modalities'])): {
'nj': row['nj'],
'nk': row['nk'],
'nkj': row['nkj']
@@ -496,7 +496,7 @@ def pvalue(result,vchi2):
for _, row in table.iterrows()
}
for i, row in result.iterrows():
- key=(str(row[vchi2]),str(row['variables']),str(row['modalities']))
+ key=(str(row[variable_cat]),str(row['variables']),str(row['modalities']))
if key in table_dict:
nj=table_dict[key]['nj']
nk=table_dict[key]['nk']
@@ -535,12 +535,12 @@ def vtest(result, v_p_value,cluster) :
Actions performed:
- * calcule the v-test on each vchi2, variables and modalities
+ * calcule the v-test on each variable_cat, variables and modalities
Args:
Returns:
- DataFrame: a pandas DataFrame containing v-test for each vchi2,
+ DataFrame: a pandas DataFrame containing v-test for each variable_cat,
variables and modalities
"""
@@ -683,11 +683,13 @@ def quanti_normality(df,quanti_var, shapiro_pvalue):
Args:
df: a pandas DataFrame containing only the quantitatives variables
- quanti_var : name of the quantitative variable
- threshold_normality : threshold choose by the user
+ quanti_var: name of the quantitative variable
+ threshold_normality: threshold choose by the user
Returns:
DataFrame: a pandas DataFrame containing the normality results
+ List: a list containing the normal variables
+ List: a list containing the non normal variables
"""
list_stat=[]
list_pvalue = []
@@ -707,6 +709,21 @@ def quanti_normality(df,quanti_var, shapiro_pvalue):
return output_shapiro, normal_variables, non_normal_variables
def quanti_homoscedasticity(df,quanti_var, variable_cat,homoscedasticity_pvalue):
+ """
+ Actions performed:
+ * Make the homoscedasticity test on each quantitative variable
+
+ Args:
+ df: a pandas DataFrame containing only the quantitatives variables
+ quanti_var: name of the quantitative variable
+ variable_cat: the categorial variable
+ homoscedasticity_normality: threshold choose by the user
+
+ Returns:
+ DataFrame: a pandas DataFrame containing the homoscedasticity results
+ List: a list containing the homoscedastic variables
+ List: a list containing the non homoscedastic variables
+ """
list_stat = []
list_pvalue = []
homoscedasticity_variables = []
@@ -735,9 +752,11 @@ def anova(df, var, variable_cat, threshold_anova):
df: a pandas DataFrame containing only the quantitatives variables
var : the quantitative variable
variable_cat : the variable to test
+ threshold_anova: threshold choose by the user
Returns:
DataFrame: a pandas DataFrame containing the anova results
+ List: a list containing the significative variables to the anova
"""
#separation of the dataframe for each variable_cat with
#a dictionary of variable_cat
@@ -788,7 +807,24 @@ def anova(df, var, variable_cat, threshold_anova):
'interpretation' : info_interpretation})
return anova, signi_anova_var
-def kruskal_wallis(df, var_non_homos, var_non_normal, variable_cat, threshold_kw):
+def kruskal_wallis(df,var_non_homos,var_non_normal,variable_cat,threshold_kw):
+ """
+ Actions performed:
+ * Make the kruskal wallis on each quantitative variable non homoscedatic
+ and/or non normal with variable_cat
+
+ Args:
+ df: a pandas DataFrame containing only the quantitatives variables
+ var_non_homos: the quantitative variable non homoscedastic
+ var_non_normal: the quantitative variable non normal
+ variable_cat: the variable to test
+ threshold_kw: threshold choose by the user
+
+ Returns:
+ DataFrame: a pandas DataFrame containing the kruskal wallis results
+ List: a list containing the significative variables to the kw and not
+ contained in the var_non_homos list
+ """
quanti_var= []
for i in var_non_homos:
if i not in quanti_var :
@@ -801,7 +837,7 @@ def kruskal_wallis(df, var_non_homos, var_non_normal, variable_cat, threshold_kw
list_interpretation = []
signi_kw_var = []
for var in quanti_var :
- df_cat = [df[df[variable_cat] == cat][var] for cat in df[variable_cat].unique()]
+ df_cat=[df[df[variable_cat]==cat][var] for cat in df[variable_cat].unique()]
stat, p_value = kruskal(*df_cat)
list_stat.append(stat)
if p_value < 0.000001 :
@@ -826,11 +862,11 @@ def quanti_analysis(df, var, signi_variable, variable_cat, thres_gaussian):
* Make the v-test and final statistics
Args:
- anova : the dataframe from the anova analysis
- df: a pandas DataFrame containing only quantitative variable
+ df: a pandas DataFrame containing only quantitative variable
var : the quantitative variables
+ signi_variable: a list containing the significative variables to the anova
+ and kruskal wallis and homoscedastic
variable_cat : the variable to test
- thres_anova : the anova threshold to continue the statistics
thres_gaussian : the gaussian threshold for the distribution
Returns:
@@ -851,7 +887,7 @@ def quanti_analysis(df, var, signi_variable, variable_cat, thres_gaussian):
I = len(df) #number of individuals
Im = len(df_na)#number of individuals that don't contain missing values
x = round(df[varia].mean(),6)#mean of the modalities
- #check if all the data in the vchi2 are missing values
+ #check if all the data in the variable_cat are missing values
ms = []
for element in dictionary :
variable_cluster.append(element)