def exploratory_analysis():
# Check if user is loggedin
if ('loggedin' in session):
# Init variables
(html_dl_link, msg_suc, msg_err, report) = (None, ) * 4
# Get session details
username = session['username']
lang = session['lang']
# Define tag category
cat_tag = 'AE'
# Connect to database
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
# Get categories of navbar
navbar_cat = datas_cat_nav(cursor, lang)
navbar_cat_name = navbar_cat[0]
navbar_cat_tag = navbar_cat[1]
navbar_cat_icon = navbar_cat[2]
navbar_cat_link = navbar_cat[3]
# Get models of navbar
navbar_models = datas_mod_nav(cursor, lang, navbar_cat_tag)
# Get settings of navbar
navbar_settings = datas_set_nav(cursor, lang)
navbar_set_name = navbar_settings[0]
navbar_set_icon = navbar_settings[1]
navbar_set_link = navbar_settings[2]
# Get category details for breadcrumb
cat_details = cards_categories(cursor, lang, cat_tag)
cat_name = cat_details[0]
cat_link = cat_details[3]
# Break connection
cursor.close()
if (request.method == 'POST'):
# Upload file
if (request.form['submit_btn'] == 'Upload Now'
or request.form['submit_btn'] == 'Envoyer maintenant'):
# All fields was complete
if (bool(request.files['file']) == 1
and bool(request.form['sep_select']) == 1):
get_upload_datas = upload_file(lang, False)
msg_err = get_upload_datas[0]
new_tmp_path = get_upload_datas[3]
df = get_upload_datas[6]
# Generate Analysis
save = ProfileReport(df, minimal=True)
# Save Report
os_path = 'C:/Users/Administrateur/Documents/EasyML/easyml/'
dir_path = 'static/dl/'
random_str = string.ascii_lowercase + string.digits
random_str = ''.join(random.sample(random_str, 16))
encode_name = hashlib.md5('exploratory_analysis'.encode())
encode_name = encode_name.hexdigest(
) + random_str + '.html'
html_dir_path = os_path + dir_path + encode_name
html_dl_link = dir_path + encode_name
save.to_file(html_dir_path)
report = True
if (lang == 'en'):
msg_suc = ('The report was successfully generated. '
'The data was automatically deleted.')
else:
msg_suc = (
'Le rapport a été produit avec succès. '
'Les données ont été automatiquement supprimées.')
# Delete file
file_remove(new_tmp_path)
else:
if (lang == 'en'):
# Submit without upload file
msg_err = (
'Please upload your data and select a separator.')
else:
msg_err = ('Veuillez télécharger vos données et '
'choisir un séparateur.')
return render_template('exploratory/exp_analysis.html',
title=cat_name,
username=username,
lang=lang,
nav_cat_name=navbar_cat_name,
nav_cat_tag=navbar_cat_tag,
nav_cat_icon=navbar_cat_icon,
nav_cat_lnk=navbar_cat_link,
nav_models=navbar_models,
nav_set_name=navbar_set_name,
nav_set_icon=navbar_set_icon,
nav_set_lnk=navbar_set_link,
cat_name=cat_name,
cat_tag=cat_tag,
cat_link=cat_link,
msg_err=msg_err,
msg_suc=msg_suc,
report=report,
data_show=html_dl_link)
else:
return redirect('404')
def home():
# Check if user is loggedin
if ('loggedin' in session):
# Get session details
username = session['username']
email = session['email']
lang = session['lang']
# Define category tag
cat_tag = 'HOME'
# Connect to database
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
# Get categories of navbar
navbar_cat = datas_cat_nav(cursor, lang)
navbar_cat_name = navbar_cat[0]
navbar_cat_tag = navbar_cat[1]
navbar_cat_icon = navbar_cat[2]
navbar_cat_link = navbar_cat[3]
# Get models of navbar
navbar_models = datas_mod_nav(cursor, lang, navbar_cat_tag)
# Get settings of navbar
navbar_settings = datas_set_nav(cursor, lang)
navbar_set_name = navbar_settings[0]
navbar_set_icon = navbar_settings[1]
navbar_set_link = navbar_settings[2]
# Get category details
cat_details = cards_categories(cursor, lang, cat_tag)
cat_name = cat_details[0]
cat_link = cat_details[3]
# Get user ID
user_infos = user_details(cursor, email, username)
user_id = user_infos[0]
# Get favorite list
favorite = favorite_list(cursor, user_id)
# Have favorite cards
if (len(favorite) > 0):
# Get favorite card datas
favorite_cards = cards_favorite(cursor, lang, username, email)
favorite_card_id = favorite_cards[0]
favorite_card_name = favorite_cards[1]
favorite_card_desc = favorite_cards[2]
favorite_card_link = favorite_cards[3]
favorite_card_img = favorite_cards[4]
# Remove favorite
if (request.method == 'POST'
and bool(request.form['model_id']) == 1):
model_id = int(request.form['model_id'])
# Favorite already existing -> remove favorite
if (model_id in favorite):
favorite_del(cursor, user_id, model_id)
# Reload page
return redirect(url_for(cat_link))
# Break connection
cursor.close()
return render_template('main/dashboard.html',
title=cat_name,
username=username,
lang=lang,
nav_cat_name=navbar_cat_name,
nav_cat_tag=navbar_cat_tag,
nav_cat_icon=navbar_cat_icon,
nav_cat_lnk=navbar_cat_link,
nav_models=navbar_models,
nav_set_name=navbar_set_name,
nav_set_icon=navbar_set_icon,
nav_set_lnk=navbar_set_link,
cat_tag=cat_tag,
card_id=favorite_card_id,
card_name=favorite_card_name,
card_desc=favorite_card_desc,
card_link=favorite_card_link,
card_image=favorite_card_img,
favorite=favorite)
else:
# Break connection
cursor.close()
return render_template('main/dashboard.html',
title=cat_name,
username=username,
lang=lang,
nav_cat_name=navbar_cat_name,
nav_cat_tag=navbar_cat_tag,
nav_cat_icon=navbar_cat_icon,
nav_cat_lnk=navbar_cat_link,
nav_models=navbar_models,
nav_set_name=navbar_set_name,
nav_set_icon=navbar_set_icon,
nav_set_lnk=navbar_set_link,
cat_tag=cat_tag,
favorite=favorite)
else:
return render_template('home.html', title='Welcome to EasyML')
def clustering_category():
# Check if user is loggedin
if ('loggedin' in session):
# Get session details
username = session['username']
email = session['email']
lang = session['lang']
# Define category tag
cat_tag = 'CLU'
# Connect to database
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
# Get categories of navbar
navbar_cat = datas_cat_nav(cursor, lang)
navbar_cat_name = navbar_cat[0]
navbar_cat_tag = navbar_cat[1]
navbar_cat_icon = navbar_cat[2]
navbar_cat_link = navbar_cat[3]
# Get models of navbar
navbar_models = datas_mod_nav(cursor, lang, navbar_cat_tag)
# Get settings of navbar
navbar_settings = datas_set_nav(cursor, lang)
navbar_set_name = navbar_settings[0]
navbar_set_icon = navbar_settings[1]
navbar_set_link = navbar_settings[2]
# Get category details
cat_details = cards_categories(cursor, lang, cat_tag)
cat_name = cat_details[0]
cat_icon = cat_details[1]
cat_desc = cat_details[2]
cat_link = cat_details[3]
cat_wiki_link = cat_details[4]
# Get models card datas
models_cards = cards_models(cursor, lang, cat_tag)
model_card_id = models_cards[0]
model_card_name = models_cards[1]
model_card_desc = models_cards[2]
model_card_link = models_cards[3]
model_card_img = models_cards[4]
# Get user ID
user_infos = user_details(cursor, email, username)
user_id = user_infos[0]
# Get favorite list
favorite = favorite_list(cursor, user_id)
# Add or remove favorite
if (request.method == 'POST' and bool(request.form['model_id']) == 1):
model_id = int(request.form['model_id'])
# Case 1: Favorite already existing -> remove favorite
if (model_id in favorite):
favorite_del(cursor, user_id, model_id)
# Reload page
return redirect(url_for(cat_link, _anchor=model_id))
# Case 2: Favorite doesn't exist -> add to favorite
elif (model_id not in favorite):
favorite_add(cursor, user_id, model_id)
# Reload page
return redirect(url_for(cat_link, _anchor=model_id))
# Break connection
cursor.close()
return render_template('common/model_category.html',
title=cat_name,
username=username,
lang=lang,
nav_cat_name=navbar_cat_name,
nav_cat_tag=navbar_cat_tag,
nav_cat_icon=navbar_cat_icon,
nav_cat_lnk=navbar_cat_link,
nav_models=navbar_models,
nav_set_name=navbar_set_name,
nav_set_icon=navbar_set_icon,
nav_set_lnk=navbar_set_link,
cat_name=cat_name,
cat_tag=cat_tag,
cat_icon=cat_icon,
cat_desc=cat_desc,
cat_link=cat_link,
cat_wiki=cat_wiki_link,
card_id=model_card_id,
card_name=model_card_name,
card_desc=model_card_desc,
card_link=model_card_link,
card_image=model_card_img,
favorite=favorite)
else:
return redirect('404')
def hierarchical_clustering():
# Check if user is loggedin
if ('loggedin' in session):
# Init variables
(data_to_html, graph_title, k, msg_suc, msg_err, msg_warn, X_col,
Y_col) = (None, ) * 8
# Init list
(cluster_pts, columns) = (list(), ) * 2
# Get session details
username = session['username']
lang = session['lang']
# Define tag category + model
cat_tag = 'CLU'
mod_tag = 'HC'
# Connect to database
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
# Get categories of navbar
navbar_cat = datas_cat_nav(cursor, lang)
navbar_cat_name = navbar_cat[0]
navbar_cat_tag = navbar_cat[1]
navbar_cat_icon = navbar_cat[2]
navbar_cat_link = navbar_cat[3]
# Get models of navbar
navbar_models = datas_mod_nav(cursor, lang, navbar_cat_tag)
# Get settings of navbar
navbar_settings = datas_set_nav(cursor, lang)
navbar_set_name = navbar_settings[0]
navbar_set_icon = navbar_settings[1]
navbar_set_link = navbar_settings[2]
# Get category details for breadcrumb
cat_details = cards_categories(cursor, lang, cat_tag)
cat_name = cat_details[0]
cat_link = cat_details[3]
# Get model details for breadcrumb
model_details = datas_model(cursor, lang, mod_tag)
model_name = model_details[0]
model_link = model_details[1]
# Break connection
cursor.close()
if (request.method == 'POST'):
# Upload file
if (request.form['submit_btn'] == 'Upload Now'
or request.form['submit_btn'] == 'Envoyer maintenant'):
# All fields was complete
if (bool(request.files['file']) == 1
and bool(request.form['sep_select']) == 1):
get_upload_datas = upload_file(lang, False)
msg_err = get_upload_datas[0]
msg_suc = get_upload_datas[1]
msg_warn = get_upload_datas[2]
global new_tmp_path
new_tmp_path = get_upload_datas[3]
global colname_list
colname_list = get_upload_datas[4]
columns = colname_list
data_to_html = get_upload_datas[5]
global df
df = get_upload_datas[6]
else:
if (lang == 'en'):
# Submit without upload file
msg_err = (
'Please upload your data and select a separator.')
else:
msg_err = (
'Veuillez télécharger vos données et choisir'
' un séparateur.')
# Model compute
if (request.form['submit_btn'] == 'Launch the model'
or request.form['submit_btn'] == 'Lancer le modèle'):
X_col = request.form['X_col']
Y_col = request.form['Y_col']
# Get colname list
columns = colname_list
# Show uploading files
data_to_html = df_html_show(df)
if (X_col == Y_col):
if (lang == 'en'):
msg_err = 'X_col and Y_col must be different.'
else:
msg_err = 'X_col et Y_col doivent être différents.'
if (msg_err is None):
# Delete feature from columns
[columns.remove(col) for col in [X_col, Y_col]]
X = df.loc[:, [X_col, Y_col]].values
# Use the dendrogram to find the optimal number of clusters
dist = linkage(X, 'ward')
# N cluster @ 50%
line_cut = statistics.mean([dist.min(), dist.max()])
# Get the best number of clusters
k = fcluster(dist, t=line_cut, criterion='distance').max()
# Training K-Means model
hc = AgglomerativeClustering(n_clusters=k,
affinity='euclidean',
linkage='ward')
y_hc = hc.fit_predict(X)
# Get plot pts
[
cluster_pts.append([
X[y_hc == i, 0].tolist(), X[y_hc == i, 1].tolist()
]) for i in np.arange(k)
]
if (lang == 'en'):
# Add graph title
graph_title = str(k) + ' clusters have been defined :'
# Success
msg_suc = ('The model was successfully calculated. '
'Your data was automatically deleted.')
else:
graph_title = str(k) + ' groupes ont été définis :'
msg_suc = (
'Le modèle a été calculé avec succès. '
'Vos données ont été automatiquement supprimées.')
# Delete file
file_remove(new_tmp_path)
return render_template('clustering/hier_clust.html',
title=model_name,
username=username,
lang=lang,
nav_cat_name=navbar_cat_name,
nav_cat_tag=navbar_cat_tag,
nav_cat_icon=navbar_cat_icon,
nav_cat_lnk=navbar_cat_link,
nav_models=navbar_models,
nav_set_name=navbar_set_name,
nav_set_icon=navbar_set_icon,
nav_set_lnk=navbar_set_link,
cat_name=cat_name,
cat_tag=cat_tag,
cat_link=cat_link,
model_name=model_name,
model_link=model_link,
msg_err=msg_err,
msg_suc=msg_suc,
msg_warn=msg_warn,
data_show=data_to_html,
df_columns=columns,
X_col=X_col,
Y_col=Y_col,
k=k,
cluster_pts=cluster_pts,
graph_title=graph_title)
else:
return redirect('404')
def polynomial_regression():
# Check if user is loggedin
if ('loggedin' in session):
# Init variables
(data_to_html, feature, graph_title,
msg_suc, msg_err, msg_warn) = (None,) * 6
# Init list
(columns, res_list, score_list) = (list(), ) * 3
# Get session details
username = session['username']
lang = session['lang']
# Define tag category + model
cat_tag = 'REG'
mod_tag = 'PR'
# Connect to database
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
# Get categories of navbar
navbar_cat = datas_cat_nav(cursor, lang)
navbar_cat_name = navbar_cat[0]
navbar_cat_tag = navbar_cat[1]
navbar_cat_icon = navbar_cat[2]
navbar_cat_link = navbar_cat[3]
# Get models of navbar
navbar_models = datas_mod_nav(cursor, lang, navbar_cat_tag)
# Get settings of navbar
navbar_settings = datas_set_nav(cursor, lang)
navbar_set_name = navbar_settings[0]
navbar_set_icon = navbar_settings[1]
navbar_set_link = navbar_settings[2]
# Get category details for breadcrumb
cat_details = cards_categories(cursor, lang, cat_tag)
cat_name = cat_details[0]
cat_link = cat_details[3]
# Get model details for breadcrumb
model_details = datas_model(cursor, lang, mod_tag)
model_name = model_details[0]
model_link = model_details[1]
# Break connection
cursor.close()
if (request.method == 'POST'):
# Upload file
if (request.form['submit_btn'] == 'Upload Now'
or request.form['submit_btn'] == 'Envoyer maintenant'):
# All fields was complete
if (bool(request.files['file']) == 1
and bool(request.form['sep_select']) == 1
):
get_upload_datas = upload_file(lang, False)
msg_err = get_upload_datas[0]
msg_suc = get_upload_datas[1]
msg_warn = get_upload_datas[2]
global new_tmp_path
new_tmp_path = get_upload_datas[3]
global colname_list
colname_list = get_upload_datas[4]
columns = colname_list
data_to_html = get_upload_datas[5]
global df
df = get_upload_datas[6]
else:
if (lang == 'en'):
# Submit without upload file
msg_err = (
'Please upload your data and select a separator.'
)
else:
msg_err = (
'Veuillez télécharger vos données et '
'choisir un séparateur.'
)
# Model compute
if (request.form['submit_btn'] == 'Launch the model'
or request.form['submit_btn'] == 'Lancer le modèle'):
feature = request.form['feature']
# Get colname list
columns = colname_list
# Show uploading files
data_to_html = df_html_show(df)
# Delete feature from columns
columns.remove(feature)
for i in columns:
x_feat = df[feature].values.reshape(-1, 1)
y_targ = df[i].values.reshape(-1, 1)
# Train Test
X_train, X_test, y_train, y_test = train_test_split(
x_feat,
y_targ,
test_size=0.33,
random_state=42
)
score_rmse = list()
min_rmse, min_deg = (math.inf,) * 2
for deg in range(1, 11):
# Train features
poly_features = PolynomialFeatures(degree=deg, include_bias=False)
x_poly_train = poly_features.fit_transform(X_train)
# Linear regression
poly_reg = LinearRegression().fit(x_poly_train, y_train)
# Compare with test data
x_poly_test = poly_features.fit_transform(X_test)
poly_predict = poly_reg.predict(x_poly_test)
poly_rmse = np.sqrt(mean_squared_error(y_test, poly_predict))
score_rmse.append(poly_rmse)
# Cross-validation of degree
if (min_rmse > poly_rmse):
min_rmse = poly_rmse
min_deg = deg
# Create Polynomial model
polynomial = PolynomialFeatures(degree=min_deg)
# Fit polynomial model
X_train = polynomial.fit_transform(X_train)
X_test = polynomial.fit_transform(X_test)
# Create linear model and fit
regressor = linear_model.LinearRegression().fit(X_train, y_train)
# Predicting test set results
y_test_pred = regressor.predict(X_test)
# Prediction
y_pred = regressor.predict(X_train)
y_pred = y_pred.tolist()
# Accuracy
r2_test = r2_score(y_test , y_test_pred) * 100
r2_train = r2_score(y_train, y_pred) * 100
res = [i, round(statistics.mean([r2_test, r2_train]), 2)]
res_list.append(res)
# Save scoring
score_list = [score[1] for score in res_list]
if (lang == 'en'):
# Add graph title
graph_title = (
'Comparison of the correlation between ' + feature +
' and the columns :'
)
# Success
msg_suc = (
'The model was successfully calculated. '
'Your data was automatically deleted.'
)
else:
graph_title = (
'Comparaison de la corrélation entre ' + feature +
' et les colonnes :'
)
msg_suc = (
'Le modèle a été calculé avec succès. '
'Vos données ont été automatiquement supprimées.'
)
# Delete file
file_remove(new_tmp_path)
return render_template(
'regression/pol_reg.html',
title = model_name,
username = username,
lang = lang,
nav_cat_name = navbar_cat_name,
nav_cat_tag = navbar_cat_tag,
nav_cat_icon = navbar_cat_icon,
nav_cat_lnk = navbar_cat_link,
nav_models = navbar_models,
nav_set_name = navbar_set_name,
nav_set_icon = navbar_set_icon,
nav_set_lnk = navbar_set_link,
cat_name = cat_name,
cat_tag = cat_tag,
cat_link = cat_link,
model_name = model_name,
model_link = model_link,
msg_err = msg_err,
msg_suc = msg_suc,
msg_warn = msg_warn,
data_show = data_to_html,
df_columns = columns,
feature = feature,
score_list = score_list,
graph_title = graph_title
)
else:
return redirect('404')
def multiple_linear_regression():
# Check if user is loggedin
if ('loggedin' in session):
# Init variables
(data_to_html, X_col, Y_col, graph_title,
msg_suc, msg_err, msg_warn) = (None,) * 7
# Init list
(columns, score_list) = (list(), ) * 2
# Get session details
username = session['username']
lang = session['lang']
# Define tag category + model
cat_tag = 'REG'
mod_tag = 'MLR'
# Connect to database
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
# Get categories of navbar
navbar_cat = datas_cat_nav(cursor, lang)
navbar_cat_name = navbar_cat[0]
navbar_cat_tag = navbar_cat[1]
navbar_cat_icon = navbar_cat[2]
navbar_cat_link = navbar_cat[3]
# Get models of navbar
navbar_models = datas_mod_nav(cursor, lang, navbar_cat_tag)
# Get settings of navbar
navbar_settings = datas_set_nav(cursor, lang)
navbar_set_name = navbar_settings[0]
navbar_set_icon = navbar_settings[1]
navbar_set_link = navbar_settings[2]
# Get category details for breadcrumb
cat_details = cards_categories(cursor, lang, cat_tag)
cat_name = cat_details[0]
cat_link = cat_details[3]
# Get model details for breadcrumb
model_details = datas_model(cursor, lang, mod_tag)
model_name = model_details[0]
model_link = model_details[1]
# Break connection
cursor.close()
if (request.method == 'POST'):
# Upload file
if (request.form['submit_btn'] == 'Upload Now'
or request.form['submit_btn'] == 'Envoyer maintenant'):
# All fields was complete
if (bool(request.files['file']) == 1
and bool(request.form['sep_select']) == 1
):
get_upload_datas = upload_file(lang, False)
msg_err = get_upload_datas[0]
msg_suc = get_upload_datas[1]
msg_warn = get_upload_datas[2]
global new_tmp_path
new_tmp_path = get_upload_datas[3]
global colname_list
colname_list = get_upload_datas[4]
columns = colname_list
data_to_html = get_upload_datas[5]
global df
df = get_upload_datas[6]
else:
if (lang == 'en'):
# Submit without upload file
msg_err = (
'Please upload your data and select a separator.'
)
else:
msg_err = (
'Veuillez télécharger vos données et '
'choisir un séparateur.'
)
# Model compute
if (request.form['submit_btn'] == 'Launch the model'
or request.form['submit_btn'] == 'Lancer le modèle'):
X_col = request.form['X_col']
Y_col = request.form['Y_col']
# Show uploading files
data_to_html = df_html_show(df)
# Get colname list
columns = colname_list
# Delete feature from columns
columns.remove(X_col)
columns.remove(Y_col)
for i in columns:
x_feat = df[[X_col, i]].values
y_targ = df[Y_col].values
# Train Test
X_train, X_test, y_train, y_test = train_test_split(
x_feat,
y_targ,
test_size=0.33,
random_state=42
)
# Create model and fit
regressor = LinearRegression().fit(X_train, y_train)
# Predicting test set results
y_test_pred = regressor.predict(X_test)
# Prediction
y_pred = regressor.predict(X_train)
y_pred = y_pred.tolist()
# Accuracy
r2_test = r2_score(y_test , y_test_pred) * 100
r2_train = r2_score(y_train, y_pred) * 100
score_list.append(round(statistics.mean([r2_test, r2_train]), 2))
columns = [X_col + ' + ' + c for c in columns]
if (lang == 'en'):
# Add graph title
graph_title = (
'Comparison of the correlation between ' + Y_col +
' and the columns :'
)
# Success
msg_suc = (
'The model was successfully calculated. '
'Your data was automatically deleted.'
)
else:
graph_title = (
'Comparaison de la corrélation entre ' + Y_col +
' et les colonnes :'
)
msg_suc = (
'Le modèle a été calculé avec succès. '
'Vos données ont été automatiquement supprimées.'
)
# Delete file
file_remove(new_tmp_path)
return render_template(
'regression/mul_lin_reg.html',
title = model_name,
username = username,
lang = lang,
nav_cat_name = navbar_cat_name,
nav_cat_tag = navbar_cat_tag,
nav_cat_icon = navbar_cat_icon,
nav_cat_lnk = navbar_cat_link,
nav_models = navbar_models,
nav_set_name = navbar_set_name,
nav_set_icon = navbar_set_icon,
nav_set_lnk = navbar_set_link,
cat_name = cat_name,
cat_tag = cat_tag,
cat_link = cat_link,
model_name = model_name,
model_link = model_link,
msg_err = msg_err,
msg_suc = msg_suc,
msg_warn = msg_warn,
data_show = data_to_html,
df_columns = columns,
X_col = X_col,
Y_col = Y_col,
score_list = score_list,
graph_title = graph_title
)
else:
return redirect('404')