upload origin

README.md

@@ -1,13 +1,13 @@
 ---
 title: DecisionTree RandomForest
-emoji: 🌖
+emoji: 📚
 colorFrom: purple
-colorTo: red
+colorTo: pink
 sdk: gradio
 sdk_version: 3.16.2
 app_file: app.py
 pinned: false
-license: apache-2.0
+license: ecl-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import pandas as pd
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import cross_val_score
+from matplotlib import pyplot as plt
+import seaborn as sns
+import gradio as gr
+import numpy as np
+
+
+missing_values = ["--"]
+data = pd.read_csv('./macau_weather.csv', na_values = missing_values)
+del data['num']
+before_rows = data.shape[0]
+data = data.dropna()
+after_rows = data.shape[0]
+adp = after_rows/before_rows*100
+edp = (before_rows - after_rows)/before_rows *100
+
+def make_ad_plot():    
+    fig = plt.figure()
+    plt.title("Percentage of available data")
+    data = [adp, edp]
+    explode = [0.2,0.2]
+    colors = sns.color_palette("Paired")
+    plt.pie(data, colors = colors, autopct = '%0.0f%%', explode = explode)
+    return fig
+
+def make_ra_table(data):
+    table_data = pd.DataFrame(columns=['Before Rain Accumulation', 'Convered Rain Accumulation'])
+    clean_data = data.copy()
+    clean_data['rain_accum'] = (clean_data['rain_accum']>1) *1
+    table_data['Before Rain Accumulation'] = data['rain_accum']
+    table_data['Convered Rain Accumulation'] = clean_data['rain_accum']
+    return table_data, clean_data
+
+def make_rc_plot():
+    corr=clean_data.corr()
+    fig = plt.figure(figsize=(8,8))
+    sns.heatmap(corr,annot=True,cmap='crest',linewidths=0.2)
+    return fig
+
+def make_r_plot(libraries, sd,cd,dd,ld,rc,rd,rl,rn,rf):
+    sns.set_theme(style="darkgrid")
+    colors = sns.color_palette("Paired")
+    rfc = RandomForestClassifier(random_state=25, n_estimators=rn, criterion=rc, max_depth=rd, min_samples_leaf=rl,max_features=rf )
+    rfc_s = pd.DataFrame(cross_val_score(rfc,X_train,y_train,cv=10),columns=['RandomForest Score'])
+    clf = DecisionTreeClassifier(random_state=25,splitter=sd,criterion=cd, max_depth=dd, min_samples_leaf=ld)
+    clf_s = pd.DataFrame(cross_val_score(clf,X_train,y_train,cv=10),columns=['DecisioTree Score'])
+    total_socre =pd.DataFrame()
+    total_socre['RandomForest'] = rfc_s
+    total_socre['DecisionTree'] = clf_s
+    
+    fig = plt.figure(figsize=(10,5))
+    for lib in libraries:
+        plt.plot(total_socre[lib],  marker = 'o')
+    
+    plt.legend(['DecisionTree Score', 'RandomForest Score'])
+
+    plt.title("Final Score ")
+    plt.ylabel("Socre")
+    plt.xlabel("No. Cross Validation")
+    return fig
+
+table_data, clean_data = make_ra_table(data)
+
+morning_features = ['air_pressure', 'aver_tem', 'humidity',
+       'sunlight_time', 'wind_direction', 'wind_speed']
+feature=clean_data[morning_features].copy()
+label = clean_data['rain_accum'].copy()
+X_train,X_test,y_train,y_test = train_test_split(feature,label,test_size=0.33,random_state=324)
+
+
+if __name__ == '__main__':
+    with gr.Blocks() as demo:
+        gr.Markdown("""
+        ## Data Collection
+        We first collect two years (2020-2021) data from [SMG](https://www.smg.gov.mo/zh/subpage/345/embed-path/p/query-weather-c_panel).
+        Below table is sample of the data we collocted
+        """
+        )
+
+        gr.Dataframe(value = data.head(), overflow_row_behaviour='show_ends'),
+        gr.Markdown("""
+                        ## Data pre-procesing: 
+                        """),        
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("""
+                            ### Data Cleaning: 
+                            We mark the **"NaN"** data as **"--"** by pandas, 
+                            and we find that there is 623 data is avaliable, anther 3 have NaN column.
+                            However, it is just 0.3% in whole data, we decide to delete these data
+                            """),
+                demo.load(fn=make_ad_plot, inputs=None, outputs=gr.Plot(label = "Pie Plot"))
+            with gr.Column():
+                gr.Markdown("""
+                            ### Data Type Conversion: 
+                            Because Decision Tree only accept discrete feature, we conver the rain accumulation. If Rain Accumulation > 1, we think the weather had rained, else it dosen't.\\
+                            Below talbe is our convered data:
+                            """),
+                gr.Dataframe(value = table_data.head(9), overflow_row_behaviour='show_ends')
+
+        gr.Markdown("""
+                        ### Feature Selection: 
+                        We choose the columns which have high correlation conefficient as feature, rain accumulation as label.
+                        """),
+        with gr.Row():
+            demo.load(fn=make_rc_plot, inputs=None, outputs=gr.Plot(label = "Feature correlation conefficient"))
+            gr.Dataframe(value = feature.head(10))
+
+        gr.Markdown("""
+                        ## Model Training: 
+                        We use DecisionTree and RandomForest to train our data
+                        ### Adjust Hyper-parameter
+                        """),
+        with gr.Box():
+            libraries = gr.CheckboxGroup(choices=["DecisionTree","RandomForest"], label="Select Model to display", value=["DecisionTree","RandomForest"])
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("""#### DecisionTree""")
+                    sd = gr.Radio(['best','random'],value="best",label="splitter of DecisionTree")
+                    cd=gr.Radio(['gini', 'entropy'],value="entropy",label="criterion of Decisiontree")
+                    dd = gr.Slider(label="max_depth of Decisiontree", value=4, minimum=1, maximum=10, step=1)
+                    ld = gr.Slider(label="min_samples_leaf of Decisiontree", value=1, minimum=1, maximum=50, step=5)
+                with gr.Column():
+                        gr.Markdown("""#### RandomForest""")
+
+                        rc=gr.Radio(['gini', 'entropy'],value="entropy",label="criterion of RandomForest")
+                        rd = gr.Slider(label="max_depth of RandomForest", value=4, minimum=1, maximum=10, step=1)
+                        rl=gr.Slider(label="min_samples_leaf of RandomForest", value=10, minimum=1, maximum=50, step=5)
+                        rn = gr.Slider(label="n_estimators of RandomForest", value=11, minimum=5, maximum=15, step=1)
+                        rf =gr.Slider(label="max_features of RandomForest", value=20,minimun=5, maximum=30, step=1)
+            train = gr.Button(value="Train")
+            train.click(fn=make_r_plot, inputs=[libraries,sd,cd,dd,ld,rc,rd,rl,rn,rf], outputs=gr.Plot(label = "Vaildation Score Plot"))
+    
+    demo.launch(share=True)

macau_weather.csv

@@ -0,0 +1,427 @@
+num,date,air_pressure,high_tem,aver_tem,low_tem,humidity,sunlight_time,wind_direction,wind_speed,rain_accum
+1,2020-10-1,1009.5,26.9,25.8,24.6,83,0.2,90,16,7.6
+2,2020-10-2,1010.9,28.8,26.7,25.5,76,6.6,90,14,0
+3,2020-10-3,1011.5,29.5,27.2,25.6,79,9.2,90,10,0
+4,2020-10-4,1010.3,32,27.8,25.9,78,9.3,202.5,7,0
+5,2020-10-5,1011.3,30.5,26.9,24.8,83,2.7,90,12,3.6
+6,2020-10-6,1013.9,27.2,25.2,23.6,80,0.4,22.5,17,1.4
+7,2020-10-7,1015,25.5,23.7,22.3,75,0,22.5,21,0
+8,2020-10-8,1015.5,26.5,23.7,22,71,5.4,22.5,20,0
+9,2020-10-9,1015.2,28.2,24.2,21.8,68,7.6,360,15,0
+10,2020-10-10,1013.1,29,24.9,22.4,68,9.5,22.5,12,0
+11,2020-10-11,1010.6,30,25.9,23.3,75,8.8,22.5,10,0
+12,2020-10-12,1008.9,30.2,27.2,24.4,73,9.9,22.5,12,0
+13,2020-10-13,1009.4,26.8,24.5,23,88,0,45,25,26.2
+14,2020-10-14,1012.4,26.1,25,23.7,82,0,67.5,28,1.8
+15,2020-10-15,1013.7,29.6,25.8,24.2,74,7.6,67.5,18,0
+16,2020-10-16,1013.6,29.6,26,24.3,76,4.4,22.5,14,0
+17,2020-10-17,1015,26.8,24.4,21.9,76,4,22.5,18,0.8
+18,2020-10-18,1015.9,27.1,23.8,20.8,72,7.9,22.5,17,0.1
+19,2020-10-19,1016.3,26.3,23,20.6,71,4.3,22.5,19,0.1
+20,2020-10-20,1015.6,27.1,23.2,20.3,69,8,360,16,0
+21,2020-10-21,1012.7,26.3,22.6,19.6,66,9.1,360,18,0
+22,2020-10-22,1010,26.4,23.1,20.7,62,8.2,360,22,0
+23,2020-10-23,1011.8,24,22.4,20.5,52,1.7,22.5,23,0
+24,2020-10-24,1014.1,23.6,22.2,19.5,56,0.1,22.5,18,0.4
+25,2020-10-25,1015,27.5,23.3,21.2,70,5.8,90,11,0
+26,2020-10-26,1013.8,27.8,23.8,21.8,78,9.6,90,11,0
+27,2020-10-27,1013,29.3,24.4,21.8,77,9.3,90,13,0
+28,2020-10-28,1015.1,24.5,23.1,21.5,84,0,22.5,12,8.2
+29,2020-10-29,1017.7,25.3,22.9,21.2,86,0.3,22.5,14,1.2
+30,2020-10-30,1018.5,26.6,23.2,21.7,82,2.6,22.5,12,0.1
+31,2020-10-31,1017.9,25.8,22.2,20.7,76,3.1,90,11,0.1
+1,2020-11-1,1016.2,27,23.4,20.9,73,8.4,22.5,6,0
+2,2020-11-2,1015.7,27.9,23.9,20.8,71,8.4,360,13,0
+3,2020-11-3,1017.3,26,22.6,19.8,70,4.6,22.5,14,0.1
+4,2020-11-4,1017.7,26.4,22.2,19.5,72,6.5,90,12,0.1
+5,2020-11-5,1017.9,26.3,22.4,19.9,70,9.6,90,10,0
+6,2020-11-6,1016.4,28.7,24.1,19.8,68,9.2,22.5,8,0
+7,2020-11-7,1016.2,29.1,25.4,22.4,58,9.5,360,13,0
+8,2020-11-8,1017.4,27.8,24.7,22.8,59,5.3,22.5,17,0
+9,2020-11-9,1018,27,23.2,21.3,54,7.4,22.5,16,0
+10,2020-11-10,1019.4,23.7,22,21,58,0,22.5,13,0
+11,2020-11-11,1021,25.6,22,20.4,64,6.8,22.5,12,0
+12,2020-11-12,1019.2,26,21.4,18.6,65,9.8,22.5,12,0
+13,2020-11-13,1017.2,25.7,22,19.3,60,0.9,22.5,14,0.1
+14,2020-11-14,1017.8,24.2,22,20.5,66,0.2,22.5,16,0.1
+15,2020-11-15,1019.4,26.1,21.9,20.1,81,2.3,22.5,10,0.4
+16,2020-11-16,1018.2,28.4,23.1,20,79,9.6,22.5,8,0
+17,2020-11-17,1015.6,25.3,23.2,21.4,81,4.5,90,13,0.1
+18,2020-11-18,1013.4,27.8,23.8,22.5,84,1.5,112.5,8,0
+19,2020-11-19,1012.2,27.9,24.2,22.4,90,3.7,157.5,6,1
+20,2020-11-20,1013,30.5,24.8,22.8,86,7.8,157.5,6,0.1
+21,2020-11-21,1014.8,23.5,22.7,22,92,0,90,13,0
+22,2020-11-22,1017.5,29.5,24.2,21.8,84,7.7,22.5,8,0.2
+23,2020-11-23,1019.7,24.1,22.5,21.4,84,0.9,90,14,0.1
+24,2020-11-24,1019.7,25.8,22.1,20.5,78,6.1,22.5,10,0
+25,2020-11-25,1019.1,26.2,22.8,20.8,79,7.7,22.5,10,0
+26,2020-11-26,1019.6,27.4,23.1,21.1,79,8,22.5,10,0
+27,2020-11-27,1021.2,23.6,21.1,18.9,71,9.3,360,15,0
+28,2020-11-28,1022.6,22.3,19,16,68,4.7,360,15,0
+29,2020-11-29,1022.4,21.9,18.2,15.9,64,4.5,360,13,0
+30,2020-11-30,1023,20.6,17.5,14.5,66,6.4,360,15,0.1
+1,2020-12-1,1022.8,21.6,17.8,14.6,66,8.8,360,12,0
+2,2020-12-2,1021.1,22.1,18,15,67,9.1,360,12,0
+3,2020-12-3,1021.9,18.7,15.9,14,65,8.7,360,17,0
+4,2020-12-4,1022.2,16.6,14.3,11.7,64,9,360,17,0
+5,2020-12-5,1022.2,18.2,14.8,11.5,64,8.6,360,14,0
+6,2020-12-6,1021,20.2,16.4,13,65,9.1,22.5,10,0
+7,2020-12-7,1021,21.7,18.4,15.5,61,3.9,337.5,11,0
+8,2020-12-8,1020.4,21,17.6,14.3,61,7.2,360,11,0
+9,2020-12-9,1018.2,21.1,18.1,15.8,67,0.8,22.5,9,0
+10,2020-12-10,1017.1,22.8,20,18,71,0.3,360,9,0.1
+11,2020-12-11,1016.2,23.3,19.9,18.7,84,0.9,22.5,7,0.1
+12,2020-12-12,1015.5,21.4,19.4,18,84,0,360,8,0.2
+13,2020-12-13,1014.8,21.3,19.8,18.9,83,0.1,112.5,11,0
+14,2020-12-14,1018.4,20,17.3,13.2,85,0,360,11,0
+15,2020-12-15,1023,14.1,12.4,10.7,74,0,360,15,0
+16,2020-12-16,1024.3,14.3,12.1,10.1,72,1,360,14,0
+17,2020-12-17,1023,14.5,12,10,71,0,360,12,0
+18,2020-12-18,1022.4,16.3,13.6,11.6,68,2.1,360,12,0
+19,2020-12-19,1024.4,14.8,12.3,9.8,61,1,360,14,0
+20,2020-12-20,1024.9,16.7,13.1,10.3,55,6,360,14,0
+21,2020-12-21,1022.8,17.7,14.2,10.6,53,7.9,360,13,0
+22,2020-12-22,1020.1,19.3,15.3,12.4,61,0.8,360,9,0
+23,2020-12-23,1017.3,20.2,17.4,14.9,72,0.2,22.5,8,0.1
+24,2020-12-24,1016.8,22.9,19,17.2,72,6,360,9,0
+25,2020-12-25,1019,20.7,17.6,16.5,74,0.7,22.5,10,0
+26,2020-12-26,1018.3,19.6,17.7,16.2,79,5.5,112.5,13,0
+27,2020-12-27,1016.2,24.7,19.9,16.7,66,9.4,360,7,0
+28,2020-12-28,1015,22.8,19.4,17.1,76,9,112.5,10,0
+29,2020-12-29,1015.2,24,20.3,17.8,75,8,112.5,7,0
+30,2020-12-30,1023.3,20.1,13.6,9.6,51,9,360,26,0
+31,2020-12-31,1027.4,13.3,9.7,6.7,37,9.1,360,22,0
+1,2021-1-1,1026,14.5,10.5,7.3,36,9.1,22.5,15,0
+2,2021-1-2,1023.2,18.2,13,9,43,9.2,22.5,10,0
+3,2021-1-3,1022.3,20.6,15.5,11.8,62,8.6,22.5,11,0
+4,2021-1-4,1021.4,21.2,17,15.3,56,0.7,22.5,7,0
+5,2021-1-5,1020.4,21.9,17.9,16,53,2.2,22.5,7,0
+6,2021-1-6,1020.2,20.2,16,14.4,69,1.9,22.5,10,0
+7,2021-1-7,1021.2,16.5,13.9,9.3,68,0.2,22.5,18,0
+8,2021-1-8,1025.6,9.4,7.5,6.1,57,0,360,25,0
+9,2021-1-9,1024.9,11.3,9.1,6.9,34,0.8,360,17,0
+10,2021-1-10,1023.7,14.2,11.4,10.3,36,0.3,22.5,14,0
+11,2021-1-11,1026.5,10.8,8.6,7.1,44,0,360,18,0.1
+12,2021-1-12,1024.3,14.8,10,6.3,33,9.2,360,16,0
+13,2021-1-13,1020.2,17.9,12.2,8.2,42,9.5,22.5,7,0
+14,2021-1-14,1018.1,20,14.2,9.6,58,7.7,157.5,6,0
+15,2021-1-15,1016.3,21.2,16.5,12.2,56,8,135,6,0
+16,2021-1-16,1017.5,21,16.6,13.2,68,5.7,90,9,0
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+19,2021-1-19,1020.5,19.8,14.7,10.9,65,8.3,22.5,8,0
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+22,2021-1-22,1013.9,25.4,20.4,17.8,77,5.4,315,7,0.1
+23,2021-1-23,1014.4,24.2,19.8,16.5,72,9.1,22.5,8,0
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+9,2021-2-9,1017.6,18.7,17.8,17.2,77,0,90,18,0.1
+10,2021-2-10,1014.2,17.4,15.2,14,94,0,360,16,38
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+14,2021-2-14,1016.5,24.3,19,15.6,79,8.8,247.5,6,0
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requirements.txt

@@ -0,0 +1,4 @@
+scikit-learn
+seaborn
+gradio
+joblib