Upload streamlit_app.py

streamlit_app.py

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+import os
+os.system('pip install pdpbox==0.2.1')
+
+from pdpbox.pdp import pdp_isolate, pdp_plot
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_absolute_error
+from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.feature_selection import SelectKBest
+from sklearn.ensemble import RandomForestRegressor
+import pandas as pd
+from numpy import mean
+import streamlit as st
+
+"""
+# IOT
+"""
+
+
+max_depth_input = st.slider("Max depth", 1, 100, 5)
+colsample_bytree_input = st.slider("Colsample bytree", 0.0, 1.0, 0.5)
+learning_rate_input = st.slider("Learning rate", 0.0, 1.0, 0.2)
+alpha_input = st.slider("Alpha", 1, 100, 10)
+n_estimators_input = st.slider("n estimators", 1, 100, 20)
+city_input = st.selectbox(
+     'Which city do you want to predict rain ?',
+     ("Canberra",
+    "Albury",
+    "Penrith",
+    "Sydney",
+    "MountGinini",
+    "Bendigo",
+    "Brisbane",
+    "Portland"), index=0)
+
+
+df = pd.read_csv("city_temperature.csv")
+
+def mergeStateToCountry():
+    df.loc[df['State'].notna(), 'Country'] = df['State']
+    df = df.loc[:, ~df.columns.str.contains('State')]
+
+i = 0
+
+for region in df["Region"].unique():
+    df["Region"] = df["Region"].replace(region, str(i))
+    i += 1
+    
+i = 0
+
+for country in df["Country"].unique():
+    df["Country"] = df["Country"].replace(country, str(i))
+    i += 1
+    
+i = 0
+
+for state in df["State"].unique():
+    df["State"] = df["State"].replace(state, str(i))
+    i += 1
+    
+i = 0
+
+for city in df["City"].unique():
+    df["City"] = df["City"].replace(city, str(i))
+    i += 1
+
+df = df.astype({"Region": "int"})
+df = df.astype({"Country": "int"})
+df = df.astype({"State": "int"})
+df = df.astype({"City": "int"})
+
+target = 'AvgTemperature'
+# Here Y would be our target
+Y = df[target]
+# Here X would contain the other column
+#X = df.loc[:, df.columns != target]
+X = df[['Month', 'Day', 'Year']]
+
+X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.25, random_state=42)
+
+y_pred = [Y_train.mean()] * len(Y_train)
+
+st.write('Baseline MAE: %f' % (round(mean_absolute_error(Y_train, y_pred), 5)))
+
+lm = make_pipeline(StandardScaler(), LinearRegression(),)
+
+lm.fit(X_train, Y_train)
+
+st.write('Linear Regression Training MAE: %f' % (round(mean_absolute_error(Y_train, lm.predict(X_train)), 5)))
+st.write('Linear Regression Test MAE: %f' % (round(mean_absolute_error(Y_val, lm.predict(X_val)), 5)))
+
+forestModel = make_pipeline(
+    SelectKBest(k="all"), 
+    StandardScaler(), 
+    RandomForestRegressor(
+        n_estimators=100,
+        max_depth=50,
+        random_state=77,
+        n_jobs=-1))
+
+forestModel.fit (X_train, Y_train)
+
+st.write('Random Forest Regressor Model Training MAE: %f' % (mean_absolute_error(Y_train, forestModel.predict(X_train))))
+st.write('Random Forest Regressor Model Test MAE: %f' % (mean_absolute_error(Y_val, forestModel.predict(X_val))))