Промежуточное.

LabWork01/FuncLoad.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
 def createDataFrame():
-    df = pd.read_csv('../res/Stores.csv')
+    df = pd.read_csv('res/Stores.csv')
 
     return df

LabWork01/LabWork5/create_plot.py

@@ -0,0 +1,198 @@
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib
+import matplotlib.pyplot as plt
+import sns
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+
+INCH = 25.4
+
+def create_plot_jpg(df: pd.DataFrame, nameFile):
+    # для сохранения диаграммы в конкретной папке
+    script_dir = os.path.dirname(__file__)
+    results_dir = os.path.join(script_dir, '../static/')
+
+    if not os.path.isdir(results_dir):
+        os.makedirs(results_dir)
+
+    # набор атрибутов - независимых переменных - площадь
+    _X = df["Store_Area"].array
+
+    # набор меток - зависимых переменных, значение которых требуется предсказать - выручка
+    _Y = df["Store_Sales"].array
+
+    # делим датафрейм на набор тренировочных данных и данных для тестов, test_size содержит определние соотношения этих наборов
+    X_train, X_test, y_train, y_test = train_test_split(_X, _Y, test_size=0.01, random_state=0)
+
+    regressor = LinearRegression()
+
+    X_train = X_train.reshape(-1, 1)
+    X_test = X_test.reshape(-1, 1)
+
+    regressor.fit(X_train, y_train)
+
+    # массив numpy, который содержит все предсказанные значения для входных значений в серии X_test
+    y_pred = regressor.predict(X_test)
+
+    df.plot(x='Store_Sales', y='Store_Area', style='o')
+
+    plt.title('Store sales / Store area')
+    plt.xlabel('Store sales')
+    plt.ylabel('Store area')
+
+    plt.savefig(results_dir + nameFile + '.jpg')
+    plt.close()
+
+    listMessages = ['Mean Absolute Error: ' + str(metrics.mean_absolute_error(y_test, y_pred)),
+                    'Mean Squared Error: ' + str(metrics.mean_squared_error(y_test, y_pred)),
+                    'Root Mean Squared Error: ' + str(np.sqrt(metrics.mean_squared_error(y_test, y_pred)))]
+
+    return listMessages
+
+# def graph_regression_plot_sns(
+#         X, Y,
+#         regression_model,
+#         Xmin=None, Xmax=None,
+#         Ymin=None, Ymax=None,
+#         display_residuals=False,
+#         title_figure=None, title_figure_fontsize=None,
+#         title_axes=None, title_axes_fontsize=None,
+#         x_label=None,
+#         y_label=None,
+#         label_fontsize=None, tick_fontsize=12,
+#         label_legend_regr_model='', label_legend_fontsize=12,
+#         s=50, linewidth_regr_model=2,
+#         graph_size=None,
+#         file_name=None):
+#     X = np.array(X)
+#     Y = np.array(Y)
+#     Ycalc = Y - regression_model(X)
+#
+#     if not (Xmin) and not (Xmax):
+#         Xmin = min(X) * 0.99
+#         Xmax = max(X) * 1.01
+#     if not (Ymin) and not (Ymax):
+#         Ymin = min(Y) * 0.99
+#         Ymax = max(Y) * 1.01
+#
+#         # график с остатками
+#     # ------------------
+#     if display_residuals:
+#         if not (graph_size):
+#             graph_size = (297 / INCH, 420 / INCH / 1.5)
+#         if not (title_figure_fontsize):
+#             title_figure_fontsize = 18
+#         if not (title_axes_fontsize):
+#             title_axes_fontsize = 16
+#         if not (label_fontsize):
+#             label_fontsize = 13
+#         if not (label_legend_fontsize):
+#             label_legend_fontsize = 12
+#         fig = plt.figure(figsize=graph_size)
+#         fig.suptitle(title_figure, fontsize=title_figure_fontsize)
+#         ax1 = plt.subplot(2, 1, 1)
+#         ax2 = plt.subplot(2, 1, 2)
+#
+#         # фактические данные
+#         ax1.set_title(title_axes, fontsize=title_axes_fontsize)
+#         sns.scatterplot(
+#             x=X, y=Y,
+#             label='data',
+#             s=s,
+#             color='red',
+#             ax=ax1)
+#         ax1.set_xlim(Xmin, Xmax)
+#         ax1.set_ylim(Ymin, Ymax)
+#         ax1.axvline(x=0, color='k', linewidth=1)
+#         ax1.axhline(y=0, color='k', linewidth=1)
+#         # ax1.set_xlabel(x_label, fontsize = label_fontsize)
+#         ax1.set_ylabel(y_label, fontsize=label_fontsize)
+#         ax1.tick_params(labelsize=tick_fontsize)
+#
+#         # график регрессионной модели
+#         nx = 100
+#         hx = (Xmax - Xmin) / (nx - 1)
+#         x1 = np.linspace(Xmin, Xmax, nx)
+#         y1 = regression_model(x1)
+#         sns.lineplot(
+#             x=x1, y=y1,
+#             color='blue',
+#             linewidth=linewidth_regr_model,
+#             legend=True,
+#             label=label_legend_regr_model,
+#             ax=ax1)
+#         ax1.legend(prop={'size': label_legend_fontsize})
+#
+#         # график остатков
+#         ax2.set_title('Residuals', fontsize=title_axes_fontsize)
+#         ax2.set_xlim(Xmin, Xmax)
+#         # ax2.set_ylim(Ymin, Ymax)
+#         sns.scatterplot(
+#             x=X, y=Ycalc,
+#             # label='фактические данные',
+#             s=s,
+#             color='orange',
+#             ax=ax2)
+#
+#         ax2.axvline(x=0, color='k', linewidth=1)
+#         ax2.axhline(y=0, color='k', linewidth=1)
+#         ax2.set_xlabel(x_label, fontsize=label_fontsize)
+#         ax2.set_ylabel(r'$ΔY = Y - Y_{calc}$', fontsize=label_fontsize)
+#         ax2.tick_params(labelsize=tick_fontsize)
+#
+#     # график без остатков
+#     # -------------------
+#     else:
+#         if not (graph_size):
+#             graph_size = (297 / INCH, 210 / INCH)
+#         if not (title_figure_fontsize):
+#             title_figure_fontsize = 18
+#         if not (title_axes_fontsize):
+#             title_axes_fontsize = 16
+#         if not (label_fontsize):
+#             label_fontsize = 14
+#         if not (label_legend_fontsize):
+#             label_legend_fontsize = 12
+#         fig, axes = plt.subplots(figsize=graph_size)
+#         fig.suptitle(title_figure, fontsize=title_figure_fontsize)
+#         axes.set_title(title_axes, fontsize=title_axes_fontsize)
+#
+#         # фактические данные
+#         sns.scatterplot(
+#             x=X, y=Y,
+#             label='фактические данные',
+#             s=s,
+#             color='red',
+#             ax=axes)
+#
+#         # график регрессионной модели
+#         nx = 100
+#         hx = (Xmax - Xmin) / (nx - 1)
+#         x1 = np.linspace(Xmin, Xmax, nx)
+#         y1 = regression_model(x1)
+#         sns.lineplot(
+#             x=x1, y=y1,
+#             color='blue',
+#             linewidth=linewidth_regr_model,
+#             legend=True,
+#             label=label_legend_regr_model,
+#             ax=axes)
+#
+#         axes.set_xlim(Xmin, Xmax)
+#         axes.set_ylim(Ymin, Ymax)
+#         axes.axvline(x=0, color='k', linewidth=1)
+#         axes.axhline(y=0, color='k', linewidth=1)
+#         axes.set_xlabel(x_label, fontsize=label_fontsize)
+#         axes.set_ylabel(y_label, fontsize=label_fontsize)
+#         axes.tick_params(labelsize=tick_fontsize)
+#         axes.legend(prop={'size': label_legend_fontsize})
+#
+#     plt.show()
+#     if file_name:
+#         fig.savefig(file_name, orientation="portrait", dpi=300)
+#
+#     return

LabWork01/LoadDB.py

@@ -13,6 +13,7 @@ from LabWork01.LabWork3.CreateGraphics import createGraphics
 from LabWork01.LabWork3.CustomGraphics import createCusGraphics
 from LabWork01.LabWork3.DeletePng import deleteAllPng
 from LabWork01.LabWork4.SiteSearch import SiteSearch
+from LabWork01.LabWork5.create_plot import create_plot_jpg
 
 app = Flask(__name__)
 
@@ -45,7 +46,7 @@ search_engine.add("https://www.kaggle.com/datasets/fedesoriano/stroke-prediction
 
 @app.route("/")
 def home():
-    return render_template('main_page.html', context=[], main_img=[], image_names=[], tableAnalys=[], titles=[''], listTypes=listTypes, countNull=countNull, firstRow=1, secondRow=4, firstColumn=1, secondColumn=4)
+    return render_template('main_page.html', context=[], main_img=[], messages=[], image_names=[], tableAnalys=[], titles=[''], listTypes=listTypes, countNull=countNull, firstRow=1, secondRow=4, firstColumn=1, secondColumn=4)
 
 @app.route("/showDiapason", methods=['GET','POST'])
 def numtext():
@@ -83,7 +84,7 @@ def numtext():
         totalList.append(listStoreSales)
 
     if int(data['firstRow']) and int(data['secondRow']) and int(data['firstColumn']) and int(data['secondColumn']):
-        return render_template('main_page.html', context=totalList, main_img=[], image_names=[], listTypes=listTypes, countNull=countNull,
+        return render_template('main_page.html', context=totalList, main_img=[], messages=[], image_names=[], listTypes=listTypes, countNull=countNull,
                                firstColumn=int(data['firstColumn']), secondColumn=int(data['secondColumn']),
                                firstRow=int(data['firstRow']), secondRow=int(data['secondRow']))
 
@@ -157,6 +158,7 @@ def analysis():
                            tableAnalysThree=[],
                            tableAnalysFour=[],
                            main_img=newCustomJpg,
+                           messages=[],
                            titles=[''],
                            listTypes=listTypes, countNull=countNull, firstRow=1,
                            secondRow=4, firstColumn=1, secondColumn=4)
@@ -184,6 +186,32 @@ def get_page_showFindURL():
 
     return render_template('showLinks.html', links=links)
 
+# 5-я лабораторная
+@app.route('/createPlotImage',  methods=['GET', 'POST'])
+def get_plot_image():
+
+    # 99%
+    # main_df = listShops.loc[listShops['Store_ID'] <= listShops.shape[0]*0.9]
+
+    # 1%
+    # support_df = listShops.loc[listShops['Store_ID'] > listShops.shape[0]*0.9]
+
+    messages = create_plot_jpg(listShops, "myPlot")
+
+    myPlotJpg = ['myPlot.jpg']
+
+    return render_template('main_page.html', context=[], image_names_start=[],
+                           image_names_addition=[],
+                           tableAnalysOne=[],
+                           tableAnalysTwo=[],
+                           tableAnalysThree=[],
+                           tableAnalysFour=[],
+                           main_img=myPlotJpg,
+                           messages=messages,
+                           titles=[''],
+                           listTypes=listTypes, countNull=countNull, firstRow=1,
+                           secondRow=4, firstColumn=1, secondColumn=4)
+
 if __name__=="__main__":
     app.run(debug=True)
 

LabWork01/templates/main_page.html

@@ -23,6 +23,9 @@
         <form action='http://127.0.0.1:5000/analysis' method=get>
             <input type=submit value='Анализ данных'>
         </form>
+        <form action='/createPlotImage' method=get>
+            <input type=submit value='Создание регрессии'>
+        </form>
         <form action="/findURL" method="get">
             <div class="mb-3">
                 <button type="submit" class="btn btn-primary mb-3">Запуск фильтра</button>
@@ -79,6 +82,11 @@
         </h3>
         {% for image_name in main_img %}
             <img src="{{ url_for('static', filename=image_name) }}" alt="{{ image_name }}">
+            {% for message in messages %}
+            <div>
+                <p>{{message}}</p>
+            </div>
+            {% endfor %}
         {% endfor %}
     </div>
     <div>