# Copyright CNRS/Inria/UNS
# Contributor(s): Eric Debreuve (since 2019), Morgane Nadal (2020)
#
# eric.debreuve@cnrs.fr
#
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import pandas as pd_
import numpy as np_
import matplotlib.pyplot as pl_
import matplotlib.gridspec as gs_
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn import tree
import scipy as si_
import seaborn as sb_
import sys as sy_
from typing import List
def PCAOnDF(df: pd_.DataFrame(),
target: str,
targets: List[str],
colors: List[str],
save_name: str = None,
title: str = "",
plot_duration: bool = True,
save_in: str = None,
three_D: bool = False,
) -> list:
'''
Perform 2D or 3D PCA on the CHO-DIO dataframe.
Save explained variance ratio as csv, plot and save the PCAs.
'''
# Separating the features from their conditions and durations
all_target = pd_.DataFrame(df.loc[:, [target]].values, columns=[target])
df_all = df.drop([target, "duration"], axis=1)
# Standardize the data
scaler = StandardScaler()
scaler.fit(df_all)
stand_df = scaler.transform(df_all)
# Create the PCA and fit the data
pca = PCA(n_components=2)
principal_components = pca.fit_transform(stand_df)
# Printing and saving the explained variance ratio
print(f"PCA explained variance ratio ({save_name}): ", pca.explained_variance_ratio_)
pca_exp_var_df = pd_.DataFrame(pca.explained_variance_ratio_, columns=["pca explained variance ration"])
pca_exp_var_df = pca_exp_var_df.rename(index={0: "principal component 1", 1: "principal component 2"})
pca_exp_var_df.to_csv(f"{save_in}\\pca_explained_variance_ratio_{save_name}.csv")
# Creating the principal component df
principal_df = pd_.DataFrame(data=principal_components, columns=['principal component 1', 'principal component 2'])
# Give the final df containing the principal component and their condition
final_df = pd_.concat([principal_df, all_target[target]], axis=1)
# Plot 2D
fig = pl_.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
ax.set_xlabel(f'Principal Component 1 - ratio = {round(pca.explained_variance_ratio_[0],2)}', fontsize=15)
ax.set_ylabel(f'Principal Component 2 - ratio = {round(pca.explained_variance_ratio_[1],2)}', fontsize=15)
ax.set_title(f'2 component PCA{title}', fontsize=20)
for tgt, color in zip(targets, colors):
idx = final_df[target] == tgt
ax.scatter(final_df.loc[idx, 'principal component 1']
, final_df.loc[idx, 'principal component 2']
, c=color
, s=30)
ax.legend(targets)
ax.grid()
if save_name is not None:
pl_.savefig(f"{save_in}\\PCA_{save_name}.png")
if plot_duration:
# Make separated plots for each duration of the experiments
fig = pl_.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
ax.set_xlabel(f'Principal Component 1 - ratio = {round(pca.explained_variance_ratio_[0],2)}', fontsize=15)
ax.set_ylabel(f'Principal Component 2 - ratio = {round(pca.explained_variance_ratio_[1],2)}', fontsize=15)
ax.set_title(f'2 component PCA{title}', fontsize=20)
new_tgts = [["CHO", "1H"], ["CHO", "3H"], ["CHO", "6H"], ["DIO", "1H"], ["DIO", "3H"], ["DIO", "6H"]]
final_df = pd_.concat([df[["condition", "duration"]], principal_df], axis=1)
for tgt, color in zip(new_tgts, ["lavender", "royalblue", "navy", "lightcoral", "firebrick", "red"]):
new_df = final_df.loc[(final_df["condition"] == tgt[0]) & (final_df["duration"] == tgt[1])]
new_df = new_df.drop(["condition", "duration"], axis=1)
ax.scatter(new_df['principal component 1']
, new_df['principal component 2']
, c=color
, s=30)
ax.legend(new_tgts)
ax.grid()
if save_name is not None:
pl_.savefig(f"{save_in}\\PCA_duration_{save_name}.png")
if three_D:
# Create the 3D PCA and fit the data
pca3d = PCA(n_components=3)
principal_components3d = pca3d.fit_transform(stand_df)
# Print and Save the explained variance ratio
print(f"3D PCA explained variance ratio ({save_name}): ", pca3d.explained_variance_ratio_)
pca3d_exp_var_df = pd_.DataFrame(pca3d.explained_variance_ratio_, columns=["3d pca explained variance ration"])
pca3d_exp_var_df = pca3d_exp_var_df.rename(index={0: "principal component 1", 1: "principal component 2", 2: "principal component 3"})
pca3d_exp_var_df.to_csv(f"{save_in}\\three_d_pca_explained_variance_ratio_{save_name}.csv")
# Creating the principal component df
principal3d_df = pd_.DataFrame(data=principal_components3d,
columns=['principal component 1', 'principal component 2', 'principal component 3'])
# Give the final df containing the principal component and their condition
final3d_df = pd_.concat([principal3d_df, all_target[target]], axis=1)
# Plot
fig = pl_.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel(f'Principal Component 1 - ratio = {round(pca3d.explained_variance_ratio_[0],2)}', fontsize=15)
ax.set_ylabel(f'Principal Component 2 - ratio = {round(pca3d.explained_variance_ratio_[1],2)}', fontsize=15)
ax.set_zlabel(f'Principal Component 3 - ratio = {round(pca3d.explained_variance_ratio_[2],2)}', fontsize=15)
ax.set_title(f'3 component PCA{title}', fontsize=20)
for tgt, color in zip(targets, colors):
idx = final3d_df[target] == tgt
ax.scatter(final3d_df.loc[idx, 'principal component 1']
, final3d_df.loc[idx, 'principal component 2']
, final3d_df.loc[idx, 'principal component 3']
, c=color
, s=30)
ax.legend(targets)
ax.grid()
if save_name is not None:
pl_.savefig(f"{save_in}\\three_d_PCA_{save_name}.png")
if plot_duration:
fig = pl_.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection="3d")
ax.set_xlabel(f'Principal Component 1 - ratio = {round(pca3d.explained_variance_ratio_[0], 2)}', fontsize=15)
ax.set_ylabel(f'Principal Component 2 - ratio = {round(pca3d.explained_variance_ratio_[1],2)}', fontsize=15)
ax.set_zlabel(f'Principal Component 3 - ratio = {round(pca3d.explained_variance_ratio_[2],2)}', fontsize=15)
ax.set_title(f'3 component PCA{title}', fontsize=20)
final3d_df = pd_.concat([df[["condition", "duration"]], principal3d_df], axis=1)
for tgt, color in zip(new_tgts, ["lavender", "royalblue", "navy", "lightcoral", "firebrick", "red"]):
new3d_df = final3d_df.loc[(final_df["condition"] == tgt[0]) & (final3d_df["duration"] == tgt[1])]
new3d_df = new3d_df.drop(["condition", "duration"], axis=1)
ax.scatter(new3d_df['principal component 1']
, new3d_df['principal component 2']
, new3d_df['principal component 3']
, c=color
, s=30)
ax.legend(new_tgts)
ax.grid()
if save_name is not None:
pl_.savefig(f"{save_in}\\three_d_PCA_duration_{save_name}.png")
def KmeansOnDF(df: pd_.DataFrame(),
nb_clusters: tuple,
target: str,
plot_bar: bool = True,
rep_on_image: bool = False,
labeled_somas=None,
elbow: bool = False,
intracluster_var: bool = True,
save_name: str = None,
save_in: str = None,
title: str = "",
duration: bool = False,
) -> KMeans:
'''
Perform kmeans on the pandas dataframe. Can find the best number of cluster with elbow method,
find the intracluster variance, and represent the result on the initial images.
Plot barplots with percentage of each label for each condition.
'''
# Separating the features from their conditions and durations
all_target = pd_.DataFrame(df.loc[:, [target]].values, columns=[target])
df2 = df.drop([target, "duration"], axis=1)
# Data standardization
scaler = StandardScaler()
stand_df = scaler.fit_transform(df2)
# Best number of clusters using Elbow method
if elbow:
wcss = [] # within cluster sum of errors(wcss)
for i in range(1, 24):
kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit(stand_df)
wcss.append(kmeans.inertia_)
pl_.figure()
pl_.plot(range(1, 24), wcss)
pl_.plot(range(1, 24), wcss, 'bo')
pl_.title('Elbow Method')
pl_.xlabel('Number of clusters')
pl_.ylabel('WCSS')
pl_.show(block=True)
pl_.close()
# Kmeans with x clusters
for nb_cluster in nb_clusters:
kmeans = KMeans(n_clusters=nb_cluster, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit_predict(stand_df)
label_df = pd_.DataFrame(data=kmeans.labels_, columns=['label'])
lab_cond_df = pd_.concat([label_df, all_target[target]], axis=1)
# Intracluster variance
if intracluster_var:
var_df = pd_.DataFrame(stand_df)
var = IntraClusterVariance(var_df, kmeans, nb_cluster, save_name)
var_df = pd_.DataFrame(var, columns=["intracluster variance"])
var_df = var_df.rename({cluster: f"label {cluster}" for cluster in range(nb_cluster)})
if save_in:
var_df.to_csv(f"{save_in}\\intracluster_variance_kmeans_{save_name}_k={nb_cluster}.csv")
# TODO Intersection of ellipsoids of the covariance matrices
# - ellipsoid equation: (x-mean)(1/Mcov)(x-mean).T <= 1
# Barplot
if plot_bar:
if duration:
fig = pl_.figure(figsize=(16, 8))
ax = fig.add_subplot(1, 1, 1)
(lab_cond_df.groupby("condition")["label"].value_counts(normalize=True).mul(100).rename(
"Percentage (%)").reset_index().pipe((sb_.barplot, "data"), x="condition", y="Percentage (%)",
hue="label", palette=sb_.color_palette("deep", n_colors=nb_cluster)))
ax.set_ylim(0, 100)
ax.set_title(f'Distribution of the clustering labels according to conditions{title}_k={nb_cluster}', fontsize=11)
ax.grid()
if save_name is not None:
pl_.savefig(f"{save_in}\\Hist_Clustering_{save_name}_k={nb_cluster}.png")
# pl_.show(block=True)
# pl_.close()
else:
fig = pl_.figure(figsize=(16, 8))
ax= fig.add_subplot(1, 1, 1)
(lab_cond_df.groupby("condition")["label"].value_counts(normalize=True).mul(100).rename(
"Percentage (%)").reset_index().pipe((sb_.barplot, "data"), x="condition", y="Percentage (%)",
hue="label", palette=sb_.color_palette("deep", n_colors=nb_cluster)))
ax.set_ylim(0, 100)
ax.grid()
ax.set_title(f'Distribution of the clustering labels according to conditions{title}_k={nb_cluster}', fontsize=11)
if save_name is not None:
pl_.savefig(f"{save_in}\\Hist_Clustering_{save_name}_k={nb_cluster}.png")
# pl_.show(block=True)
# pl_.close()
lab_cond_df2 = pd_.concat((lab_cond_df, df[["duration"]]), axis=1)
for dur in df["duration"].unique():
dur_df = lab_cond_df2.loc[lab_cond_df2["duration"] == dur]
fig = pl_.figure(figsize=(16, 8))
ax = fig.add_subplot(1, 1, 1)
(dur_df.groupby("condition")["label"].value_counts(normalize=True).mul(100).rename(
"Percentage (%)").reset_index().pipe((sb_.barplot, "data"), x="condition", y="Percentage (%)",
hue="label", palette=sb_.color_palette("deep", n_colors=nb_cluster)))
ax.set_ylim(0, 100)
ax.grid()
ax.set_title(f'Distribution of the clustering labels according to conditions{title}_k={nb_cluster} - duration = {dur}', fontsize=11)
if save_name is not None:
pl_.savefig(f"{save_in}\\Hist_Clustering_{save_name}_dur_{dur}_k={nb_cluster}.png")
# pl_.show(block=True)
# pl_.close()
if save_name is not None:
pl_.savefig(f"{save_in}\\Hist_duration_Clustering_{save_name}_k={nb_cluster}.png")
print(f"Saved in {save_in}")
# pl_.show(block=True)
# pl_.close()
# Representation on the image
if rep_on_image:
RepresentationOnImages(labeled_somas, kmeans, nb_cluster)
return kmeans
def IntraClusterVariance(df: pd_.DataFrame(), kmeans: KMeans(), nb_cluster: int, save_name: str = "") -> list:
'''
Return the intracluster variance of a given cluster found by kmeans.
'''
var = []
# TODO change to .inertia_
for cluster in range(nb_cluster):
soma_cluster = [indx for indx, value in enumerate(kmeans.labels_) if value == cluster]
mean_cluster = np_.average([df.iloc[row, :] for row in soma_cluster], axis=0) # TODO .cluster_centers_
variance = sum([np_.linalg.norm(df.iloc[row, :] - mean_cluster) ** 2 for row in soma_cluster]) / (len(soma_cluster) - 1)
var.append(variance)
print(f"Intracluster variance for {nb_cluster} clusters ({save_name}) :", var)
return var
def RepresentationOnImages(labeled_somas, kmeans, nb_cluster):
'''
Represent the result of kmeans on labeled image. IN DVPT. Only available for a kmean intra-image.
'''
clustered_somas = labeled_somas.copy()
clustered_somas = np_.amax(clustered_somas, axis=0)
for indx, value in enumerate(kmeans.labels_):
for indx_axe, axe in enumerate(clustered_somas):
for indx_pixel, pixel in enumerate(axe):
if pixel == indx + 1:
clustered_somas[indx_axe][indx_pixel] = value + 1
pl_.imshow(clustered_somas, cmap="tab20")
pl_.title(f"n cluster = {nb_cluster}")
pl_.show(block=True)
pl_.close()
def FeaturesStatistics(df: pd_.DataFrame(),
save_in: str = None,
title: str = "",
describe: bool = True,
heatmap: bool = True,
drop_feat: bool = True,
distribution: bool = True,
stat_test: bool = True,
decision_tree: bool = True,
):
'''
Return the statistics allowing the user to choose the most relevant features to feed ML algorithms for ex.
Statistical description, correlation heatmap, dropping correlated features or features with little difference between the two conditions,
Plotting features distribution and boxplots, performing Kolmogorov-Smirnov two-sample test and Wilcoxon signed-rank two-sample test.
Can draw a decision tree.
'''
#
# Overview of the basic stats on each columns of df
if describe:
description = df.describe()
if save_in is not None:
description.to_csv(f"{save_in}\\df_stat_description.csv")
# Data formatting
df_scalar = df.drop(["duration", "condition"], axis=1)
df_cond = df.drop(["duration"], axis=1)
df_groupby_dur = df.groupby("duration")
# Compute the correlation matrix
corr_matrix = df_scalar.corr().abs()
if heatmap:
# Plot heat map with correlation matrix btw features
print("Heat map with correlation matrix btw features")
# Generate a mask for the upper triangle
mask = np_.triu(np_.ones_like(corr_matrix, dtype=np_.bool))
# Set up the figure
fig, ax = pl_.subplots(figsize=(13, 9))
# Generate a custom diverging colormap
cmap = sb_.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sb_.heatmap(corr_matrix, mask=mask, cmap=cmap, center=0, square=True, linewidths=.5, cbar_kws={"shrink": .5}, xticklabels=False)
ax.set_title(f'Features correlation heat map{title}', fontsize=20)
if save_in is not None:
pl_.savefig(f"{save_in}\\Features correlation heat map{title}.png")
pl_.close()
if stat_test:
# Create dictionaries to store the statistics and p-values
dict_ks = {}
dict_wx = {}
dict_ks_dur = {}
dict_wx_dur = {}
for column in df_scalar.columns:
# For each feature, perform a statistical test to know if the feature distribution or median is different btw CHO and DIO conditions
cond_col_df = pd_.concat((df_scalar[column], df_cond["condition"]), axis=1)
# Separate the conditions
CHO = cond_col_df.loc[cond_col_df['condition'] == "CHO"]
CHO = np_.asarray(CHO[column])
DIO = cond_col_df.loc[cond_col_df['condition'] == "DIO"]
DIO = np_.asarray(DIO[column])
# Compare distribution between conditions (goodness of fit)
ks = si_.stats.ks_2samp(CHO, DIO)
dict_ks[column] = ks
# Compare median between conditions
wx = si_.stats.ranksums(CHO, DIO)
dict_wx[column] = wx
# Plot the p-values
fig = pl_.figure(constrained_layout=False, figsize=(15,8))
gs = fig.add_gridspec(ncols=2, nrows=1)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
#
ax1.set_title(f"Kolmogorov-Smirnov p-value btw conditions - {column}", fontsize=13, pad=20)
ax1.grid()
ax1.set_xlabel("Duration")
ax1.set_yscale("log")
ax1.set_ylabel("p-value")
#
ax2.set_title(f"Wilcoxon signed-rank p-value btw conditions - {column}", fontsize=13, pad=20)
ax2.grid()
ax2.set_xlabel("Duration")
ax2.set_yscale("log")
ax2.set_ylabel("p-value")
# Do the same thing than above but separate durations between conditions
for duration, values in df_groupby_dur:
if duration not in dict_ks_dur:
dict_ks_dur[duration] = {}
dict_wx_dur[duration] = {}
duration_df = values.drop(["duration"], axis=1)
CHO_ = duration_df.loc[duration_df['condition'] == "CHO"]
DIO_ = duration_df.loc[duration_df['condition'] == "DIO"]
CHO_ = CHO_.drop(["condition"], axis=1)
DIO_ = DIO_.drop(["condition"], axis=1)
CHO_ = np_.asarray(CHO_[column])
DIO_ = np_.asarray(DIO_[column])
# Compare distribution
ks2 = si_.stats.ks_2samp(CHO_, DIO_)
dict_ks_dur[duration][column] = ks2
# Compare median
wx2 = si_.stats.ranksums(CHO_, DIO_)
dict_wx_dur[duration][column] = wx2
ax1.plot([f"{duration}" for duration, _ in df_groupby_dur], [dict_ks_dur[duration][column][1] for duration, _ in df_groupby_dur], "g")
ax1.plot([f"{duration}" for duration, _ in df_groupby_dur], [dict_ks_dur[duration][column][1] for duration, _ in df_groupby_dur], 'ko')
ax2.plot([f"{duration}" for duration, _ in df_groupby_dur], [dict_wx_dur[duration][column][1] for duration, _ in df_groupby_dur], "y")
ax2.plot([f"{duration}" for duration, _ in df_groupby_dur], [dict_wx_dur[duration][column][1] for duration, _ in df_groupby_dur], 'ko')
pl_.tight_layout()
if save_in:
pl_.savefig(f"{save_in}\\p_values_duration_{column}")
pl_.close()
# Reformat the data to save it as csv
df_ks = pd_.DataFrame.from_dict(data=dict_ks)
df_ks = df_ks.rename(index={0: "Kolmogorov-Smirnov statistic", 1: "Kolmogorov-Smirnov p-value"})
df_wx = pd_.DataFrame.from_dict(data=dict_wx)
df_wx = df_wx.rename(index={0: "Wilcoxon statistic", 1: "Wilcoxon p-value"})
df_ks_dur = pd_.DataFrame()
df_wx_dur = pd_.DataFrame()
for key in dict_ks_dur.keys():
df_ks_dur = pd_.concat((df_ks_dur, pd_.DataFrame.from_dict(data=dict_ks_dur[key])))
df_ks_dur = df_ks_dur.rename(index={0: f"{key} - Kolmogorov-Smirnov statistic", 1: f"{key} - Kolmogorov-Smirnov p-value"})
df_wx_dur = pd_.concat((df_wx_dur, pd_.DataFrame.from_dict(data=dict_wx_dur[key])))
df_wx_dur = df_wx_dur.rename(index={0: f"{key} - Wilcoxon statistic", 1: f"{key} - Wilcoxon p-value"})
stat_tests_df1 = pd_.concat((df_ks, df_wx)) # KS and Wx all durations
stat_tests_df2 = pd_.concat((df_ks_dur, df_wx_dur)) # KS and Wx for each duration
if save_in:
stat_tests_df1.to_csv(f"{save_in}\\stat_test_KS_wilcoxon_all.csv")
stat_tests_df2.to_csv(f"{save_in}\\stat_test_KS_wilcoxon_for_each_duration.csv")
if drop_feat:
# Drop highly correlated features
print("Drop highly correlated features")
# Select upper triangle of correlation matrix
upper = corr_matrix.where(np_.triu(np_.ones(corr_matrix.shape), k=1).astype(np_.bool))
# Find index of feature columns with correlation greater than 0.9
to_drop = [column for column in upper.columns if any(upper[column] > 0.9)]
# Drop features
drop_HCF_df = df_scalar.drop(df[to_drop], axis=1)
# Drop column with null variance
drop_HCF_df = drop_HCF_df.loc[:, drop_HCF_df.var() != 0.0]
if stat_test:
# drop non significant features (distribution and mean not different btw CHO and DIO)
drop_HCF_df_6H = drop_HCF_df.copy()
# based on all durations;
to_drop =[column for column in drop_HCF_df.columns if (stat_tests_df1.loc["Kolmogorov-Smirnov p-value", column] > 1.0e-2) and (stat_tests_df1.loc["Wilcoxon p-value", column] > 1.0e-2)]
drop_HCF_df = drop_HCF_df.drop(drop_HCF_df[to_drop], axis=1)
# only based on 6H duration;
to_drop_6H =[column for column in drop_HCF_df_6H.columns if (stat_tests_df2.loc["6H - Kolmogorov-Smirnov p-value", column] > 1.0e-3) and (stat_tests_df2.loc["6H - Wilcoxon p-value", column] > 1.0e-3)]
drop_HCF_df_6H = drop_HCF_df_6H.drop(drop_HCF_df_6H[to_drop_6H], axis=1)
drop_HCF_df_6H = pd_.concat((df[["condition", "duration"]], drop_HCF_df_6H), axis=1)
if save_in:
drop_HCF_df_6H.to_csv(f"{save_in}\\df_drop_highly_corr_feat_6H.csv")
print(f"Selection of features with distribution and median of CHO vs DIO different in: {save_in}")
# Add the condition and duration
drop_HCF_df = pd_.concat((df[["condition", "duration"]], drop_HCF_df), axis=1)
if save_in:
drop_HCF_df.to_csv(f"{save_in}\\df_drop_highly_corr_feat.csv")
print(f"Selection of low correlated features in: {save_in}")
# Statistics for each features
if distribution:
for column in df_scalar.columns:
cond_col_df = pd_.concat((df_scalar[column], df_cond["condition"]), axis=1)
fig = pl_.figure(constrained_layout=False)
gs = fig.add_gridspec(ncols=2, nrows=1)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1])
# Plot a histogram and kernel density estimate
print(f"Plot a histogram and kernel density estimate for feature {column}")
CHO = cond_col_df.loc[cond_col_df['condition'] == "CHO"]
DIO = cond_col_df.loc[cond_col_df['condition'] == "DIO"]
sb_.distplot(CHO[[column]], color="b", ax=ax1)
sb_.distplot(DIO[[column]], color="r", ax=ax1)
# Draw a boxplot
print(f"Plot a boxplot for feature {column}")
sb_.boxplot(data=df_cond, x="condition", y=column, hue="condition", palette=["b", "r"], ax=ax2)
ax1.set_title(f'{column} distribution{title}', fontsize=11)
ax2.set_title(f'{column} boxplot{title}', fontsize=11)
pl_.tight_layout()
if save_in is not None:
pl_.savefig(f"{save_in}\\feat_distrib_{column}.png")
pl_.close()
# Decision tree
if decision_tree:
# Test btw CHO and DIO
dt_df = df.drop(["condition", "duration"], axis=1)
clf = tree.DecisionTreeClassifier(max_depth=4)
clf = clf.fit(dt_df, df["condition"])
fig = pl_.figure(figsize=(150, 65))
tree.plot_tree(clf, feature_names=df.columns, class_names=["CHO", "DIO"], filled=True, rounded=True, fontsize=60)
fig.suptitle(f"Decision tree all durations", fontsize=120)
if save_in:
pl_.savefig(f"{save_in}\\Decision_tree_all_durations_{title}.png")
pl_.close()
# Test btw CHO and DIO depending on duration
for duration, values in df_groupby_dur:
duration_df = values.drop(["duration", "condition"], axis=1)
clf = tree.DecisionTreeClassifier(max_depth=3)
clf = clf.fit(duration_df, values["condition"])
fig = pl_.figure(figsize=(30, 16))
tree.plot_tree(clf, feature_names=df.columns, class_names=["CHO", "DIO"], filled=True, rounded=True, fontsize=8)
fig.suptitle(f"Decision tree {duration}", fontsize=16)
if save_in:
pl_.savefig(f"{save_in}\\Decision_tree_{duration}_{title}.png")
pl_.close()
if __name__ == "__main__":
#
# os.chdir("path")
## If need to concatenate files:
# all_filenames = [i for i in glob.glob('*.{}'.format("csv"))]
# print(all_filenames)
# df = pd_.concat([pd_.read_csv(f, index_col=0) for f in all_filenames])
# df.to_csv(".\combined_features.csv")
## If use labeled somas:
# labeled_somas = np_.load("path.npy")
# df = pd_.read_csv(".\combined_features.csv", index_col=0)
## Parameters
path = sy_.argv[1]
save_in = sy_.argv[2]
## DF cleaning
df0 = pd_.read_csv(f"{path}\\features.csv",
# index_col=0,
)
df = df0.drop(["Unnamed: 0"], axis=1)
## Statistical analysis
# For the moment drop the columns with non scalar values, and un-useful values
# - TO BE CHANGED TODO (use distance metrics such as bhattacharyya coef, etc)
df = df.drop(["soma uid",
"spherical_angles_eva", "spherical_angles_evb",
"hist_lengths", "hist_lengths_P", "hist_lengths_S",
"hist_curvature", "hist_curvature_P", "hist_curvature_S"],
axis=1)
df = df.dropna(axis=0, how="any")
# -- PCA with all the features
print("\nALL FEATURES\n")
# Between the two conditions, regardless the duration of experiment (2 conditions, all durations)
PCAOnDF(df,
target="condition",
targets=["CHO", "DIO"],
colors=["b", "r"],
save_name="all_features",
save_in=save_in,
three_D=True,
)
# Between the two conditions, for each duration (2 conditions, 3 durations)
groupby_duration = df.groupby("duration")
for duration, values in groupby_duration:
## duration: str, values: pd_.DataFrame()
PCAOnDF(values,
target="condition",
targets=["CHO", "DIO"],
colors=["b", "r"],
save_name=f"{duration}_features",
save_in=save_in,
title=f" - {duration} Sample",
plot_duration=False,
three_D=True,
)
# -- K-means with all the features (2 conditions)
# Test for multiple glial populations
# Between the two conditions, regardless the duration of experiment (2 conditions, all durations)
kmeans = KmeansOnDF(df,
target="condition",
nb_clusters=(2, 3, 4, 5),
elbow=True,
intracluster_var=True,
plot_bar=True,
save_name="all_features_multiple_pop",
save_in=save_in,
)
# Between the two conditions, for each duration (2 conditions, 3 durations)
groupby_duration = df.groupby("duration")
for duration, values in groupby_duration:
kmeans = KmeansOnDF(values,
target="condition",
nb_clusters=(2, 3, 4, 5),
elbow=False,
intracluster_var=True,
plot_bar=True,
save_name=f"{duration}_features_multiple_pop",
title=f" - {duration} Sample",
duration=True,
save_in=save_in,
)
# -- Various plots to analyse the data and find discriminant features by statistical analysis
print("\nFEATURE SELECTION\n")
FeaturesStatistics(df,
save_in=save_in,
describe=True,
heatmap=True,
distribution=True,
stat_test=True,
drop_feat=True,
decision_tree=False,
)
## TODO: Enter selected features here
# selected_features = []
# selected_df = df[selected_features]
## TODO Or use the csv with dropped features
selected_df = pd_.read_csv(f"{save_in}\\df_drop_highly_corr_feat.csv")
# selected_df = pd_.read_csv(f"{save_in}\\df_drop_highly_corr_feat_6H.csv")
## If an error raises, only run the part until FeaturesStatistics included, and then run the last part.
# if other columns need to be dropped:
try:
to_drop = ["Unnamed: 0", "min_curvature"]
selected_df = selected_df.drop(to_drop, axis=1)
except:
selected_df = selected_df.drop(["Unnamed: 0"], axis=1)
# -- PCA with all the features
print("\nSELECTED FEATURES\n")
# Between the two conditions, regardless the duration of experiment (2 conditions, all durations)
PCAOnDF(df,
target="condition",
targets=["CHO", "DIO"],
colors=["b", "r"],
save_name="all_selected_features",
save_in=save_in,
three_D=True,
)
# Between the two conditions, for each duration (2 conditions, 3 durations)
groupby_duration = df.groupby("duration")
for duration, values in groupby_duration:
# duration: str, values: pd_.DataFrame()
PCAOnDF(values,
target="condition",
targets=["CHO", "DIO"],
colors=["b", "r"],
save_name=f"{duration}_selected_features",
save_in=save_in,
title=f" - {duration} Sample - selected features",
plot_duration=False,
three_D=True,
)
# -- K-means with all the features (2 conditions)
# Between the two conditions, regardless the duration of experiment (2 conditions, all durations)
kmeans = KmeansOnDF(df,
target="condition",
nb_clusters=(2,3,4,5),
elbow=False,
intracluster_var=True,
plot_bar=True,
save_name="all_selected_features",
save_in=save_in,
)
# Between the two conditions, for each duration (2 conditions, 3 durations)
groupby_duration = df.groupby("duration")
for duration, values in groupby_duration:
kmeans = KmeansOnDF(values,
target="condition",
nb_clusters=(2,3,4,5),
elbow=False,
intracluster_var=True,
plot_bar=True,
save_name=f"{duration}_selected_features",
save_in=save_in,
title=f" - {duration} Sample - selected features",
duration=True,
)
## TODO: Random forests ?