def feature_selection_pipeline_from_file():
#get data
dataset = refactor_labels(get_data(path, 'Sheet1'), group_column)
# all the visualizations
auto_visualize_features(dataset.drop(subject_number_column, axis = 1))
#remove missing values columns
non_missing_values_treshold = len(dataset.index) * 0.99
dataset.dropna(axis=1, thresh=non_missing_values_treshold, inplace=True)
#impute missing values
dataset.fillna(dataset.mean(), inplace=True)
#set X
X = dataset.drop([group_column, subject_number_column], 1)
sbj = dataset[subject_number_column]
Y = dataset[group_column]
names = list(X)
# standartize X
X = pd.DataFrame(preprocessing.MinMaxScaler().fit_transform(X))
X.columns = names
print("p0", X.shape)
#cutoff by variance
variance_threshold = 0.05
variance_cutoff = VarianceThreshold(threshold=variance_threshold)
variance_cutoff.fit_transform(X)
print("p1", X.shape)
#cutoff high correlation
corr_matrix = X.corr().abs()
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
to_drop = [column for column in upper.columns if any(upper[column] > 0.7)]
X.drop(to_drop, axis = 1, inplace=True)
print("p2",X.shape)
#random forest
k_best_features = 42
feature_importance = random_forest_selection.get_feature_importance(X,Y)
random_forest_selection.save_feature_importance(feature_importance_txt_path, feature_importance, list(X))
processed_dataframe, X = random_forest_selection.get_k_most_important_features(X,Y,k_best_features,feature_importance)
print("p3", processed_dataframe.shape)
processed_dataframe.to_csv(processed_dataframe_path)
#PCA
pca = PCA_Obj(X)
pca.explained_variance_graph(pca_explained_variance_graph_path)
pca.print_components()
n_components = 12
X = pca.create_pca(n_components)
pca.save_pca_data(features_after_pca, Y=Y)
print("p4", X.shape)
def __init__(self, data = None, path=None, remove_missing_values=1, group_column='group', subject_number_column= 'Subject_Number'):
if path:
self.dataset = refactor_labels(get_data(path, 'Sheet1'), group_column)
features_df = self.dataset.drop([group_column, subject_number_column], 1)
if not data is None:
self.dataset = features_df = data
if remove_missing_values:
features_df = features_df.dropna(axis=1)
self.X = features_df.values
def run_stuff ():
dataset = refactor_labels(get_data("C:\\Users\\user\\PycharmProjects\\AnxietyClassifier(2)\Alls_data_NO_specific_vars_corr.xlsx", "Sheet1"),"group")
dataset = imputing_avarage(dataset)
features_df = dataset.drop(['Age','group','PHQ9','Subject_Number'],1)
X = features_df.values
X = StandardScaler().fit_transform(X)
#X = array[:,3:116]
pca = RandomizedPCA(50)
pca.fit(X)
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of components')
plt.ylabel('cumulative explained variance');
plt.show()
def feature_selection_pipeline_from_file():
#get data
dataset = refactor_labels(get_data(path, 'Sheet1'), group_column)
# all the visualizations
#auto_visualize_features(dataset.drop([subject_number_column], 1))
#remove missing values columns
non_missing_values_treshold = len(dataset.index) * 0.99
dataset.dropna(axis=1, thresh=non_missing_values_treshold, inplace=True)
#impute missing values
dataset.fillna(dataset.mean(), inplace=True)
#set X
X = dataset.drop([group_column, subject_number_column], 1)
sbj = dataset[subject_number_column]
Y = dataset[group_column]
# standartize X
X = pd.DataFrame(preprocessing.MinMaxScaler().fit_transform(X))
#cutoff by variance
variance_threshold = 0.03
variance_cutoff = VarianceThreshold(threshold=variance_threshold)
variance_cutoff.fit_transform(X)
print("p1", X.shape)
#cutoff high correlation
corr_matrix = X.corr().abs()
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
to_drop = [column for column in upper.columns if any(upper[column] > 0.8)]
X.drop(X.columns[to_drop], 1, inplace=True)
print("p2",X.shape)
#save new df
processed_dataframe = pd.concat([X, Y, sbj], axis=1)
processed_dataframe.to_csv(processed_dataframe_path)
#random forest
if random_forest:
k_best_features = 31
feature_importance = random_forest_selection.get_feature_importance(X,Y)
random_forest_selection.save_feature_importance(feature_importance_txt_path, feature_importance)
processed_dataframe, X = random_forest_selection.get_k_most_important_features(X,Y,k_best_features,feature_importance)
processed_dataframe.to_csv(processed_dataframe_path)
print("p4", processed_dataframe.shape)
def visualize_features_interactivly():
files = [file_a, file_b, file_c, file_d]
file_num = input(
"which file's features do you wanna see?\n {}\n".format(files))
if file_num == 'a':
visualization_object = DataVisualizationObj(get_data(file_a, 'Sheet1'))
elif file_num == 'b':
visualization_object = DataVisualizationObj(get_data(file_b, 'Sheet1'))
elif file_num == 'c':
visualization_object = DataVisualizationObj(get_data(file_c, 'Sheet1'))
elif file_num == 'd':
visualization_object = DataVisualizationObj(get_data(file_d, 'Sheet1'))
else:
return
while True:
func_dict = {
1: visualization_object.create_binary_hist,
2: visualization_object.print_data,
3: visualization_object.plot_data,
4: visualization_object.plot_corr,
5: visualization_object.plot_correlation_matrix
}
try:
vis_type = int(
input(
"which visualization func do you wanna use?\n {}\n".format(
func_dict)))
except ValueError:
return
func_dict[vis_type]()
def __init__(self,
data=None,
path=None,
remove_missing_values=1,
group_column='group',
subject_number_column='Subject_Number'):
if path:
self.dataset = refactor_labels(get_data(path, 'Sheet1'),
group_column)
features_df = self.dataset.drop(
[group_column, subject_number_column], 1)
if not data is None:
self.dataset = features_df = data
if remove_missing_values:
features_df = features_df.dropna(axis=1)
self.X = features_df.values
def auto_visualize_features(data=None,
saving_path="subject_features_before_selection",
create_plots=1):
file = r"C:\PycharmProjects\AnxietyClassifier\ExtractedFeaturesFiles\extracted_features_subjects_set_Updated,with_outlier_subjects_False_with_9029,9014,2018-10-29.xlsx"
if not data is None:
visualization_object = DataVisualizationObj(data)
else:
visualization_object = DataVisualizationObj(get_data(file, 'Sheet1'))
path = r"C:\PycharmProjects\AnxietyClassifier\visualizations\high_low_plots_4,11"
#visualization_object.detect_outliers()
#visualization_object.print_missing_values()
#visualization_object.describe()
#visualization_object.print_variance(path=path)
# visualization_object.detect_outliers(path=path)
#visualization_object.plot_correlation_matrix(path=path)
if create_plots:
visualization_object.create_binary_hist(path=path)
visualization_object.create_two_hists_by_group(path=path)
from DataImporting.ImportData import get_data
from DataImporting.ImportData import refactor_labels
import numpy as np
from scipy import stats
file_g = r"C:\PycharmProjects\AnxietyClassifier\ExtractedFeatures\data_features_for_each_matrix.xlsx"
group_column = "group"
df = refactor_labels(get_data(file_g, 'Sheet1'), group_column)
print("df.shape\n",df.shape)
df = df.dropna(axis=1)
z = np.abs(stats.zscore(df))
threshold = 3
print(np.where(z > threshold))
#
# print("names\n", list(df))
# print("count missing\n", df.isnull().sum())
# print("\ndf.info()\n",df.info())
# print("\ndf.describe()\n", df.describe())
# print("\ndf groupby count\n", df.groupby(group_column)[group_column].count())
# print("\ndf groupby describe\n", df.groupby(group_column).describe())
def meow ():
dataset = refactor_labels(get_data("C:\\Users\\user\\PycharmProjects\\AnxietyClassifier(2)\Alls_data_NO_specific_vars_corr.xlsx", "Sheet1"),"group")
return PCA_transforme(dataset,6)
