Initial commit

app.py

@@ -0,0 +1,337 @@
+import pandas as pd
+from h2o_wave import main, app, Q, ui, data
+from sklearn.ensemble import RandomForestClassifier
+
+# Global variables to store the data
+df = None
+test_df = None
+train = None
+test = None
+
+
+@app('/waveApp')
+async def serve(q: Q):
+    global df, test_df, train, test
+
+    # Initialization and setting up the UI
+    if not q.client.initialized:
+        setup_ui(q)
+    # Handling user actions when uploading the dataset
+    elif q.args.upload:
+        upload_dataset(q)
+    # Previewing the dataset
+    elif q.args.preview:
+        preview_dataset(q)
+    # Handling user actions after uploading the dataset
+    elif q.args.user_files:
+        file_path = await q.site.download(q.args.user_files[0], '.')
+        df = load_data(file_path)
+        print(df.columns)
+        print('Preview Dataset')
+        print(df.head(10))
+        preview_dataset(q)
+    # Plotting data distribution for specific columns
+    elif q.args.plot:
+        plot_view(q)
+    # Preprocessing the data
+    elif q.args.preprocess:
+        preprocessing_data(q)
+    # Training the machine learning model
+    elif q.args.train:
+        train_model(q)
+    else:
+        upload_dataset(q)
+    await q.page.save()
+
+
+# Function to load data from CSV file
+def load_data(file_path):
+    data = pd.read_csv(file_path)
+    return data
+
+
+# Function to load the test dataset from CSV file
+def test_data():
+    tdata = pd.read_csv('test.csv')
+    return tdata
+
+
+# Function to set up the initial UI components
+def setup_ui(q):
+    q.page["header"] = ui.header_card(
+        box="1 1 12 1",
+        title='Wave App',
+        subtitle='H2O.ai Technical Assignment',
+    )
+
+    q.page["tabs"] = ui.tab_card(
+        box="1 2 9 1",
+        items=[
+            ui.tab(name="upload", label="Upload Dataset"),
+            ui.tab(name="preview", label="Preview Dataset"),
+            ui.tab(name="plot", label="Plot View"),
+            ui.tab(name="preprocess", label="Preprocess Data"),
+            ui.tab(name="train", label="Train and Predict Model"),
+        ],
+    )
+
+    q.page['footer'] = ui.footer_card(
+        box='1 10 12 1',
+        caption='**Developed by Himidiri Himakanika**',
+    )
+
+    q.client.initialized = True
+
+
+# Function to create a markdown table from DataFrame
+def make_markdown_row(values):
+    return f"| {' | '.join([str(x) for x in values])} |"
+
+
+def make_markdown_table(fields, rows):
+    return '\n'.join([
+        make_markdown_row(fields),
+        make_markdown_row(['---'] * len(fields)),
+        '\n'.join([make_markdown_row(row) for row in rows]),
+    ])
+
+
+# Function to create a markdown table for data preview
+def make_preprocess_table(df):
+    markdown_table = make_markdown_table(fields=df.columns.tolist(), rows=df.head(10).values.tolist())
+    return markdown_table
+
+
+# Function to handle dataset upload
+def upload_dataset(q):
+    del q.page['preview_dataset']
+    del q.page['plot_view']
+    del q.page['education_plot']
+    del q.page['occupation_plot']
+    del q.page['age_plot']
+    del q.page['target_vs_age_plot']
+    del q.page['target_vs_hours_plot']
+    del q.page['preprocessing_data']
+    del q.page['train_model']
+    q.page['upload_dataset'] = ui.form_card(
+        box='1 3 12 7',
+        items=[
+            ui.text_l('**UPLOAD DATASET**'),
+            ui.file_upload(name='user_files', label='Upload census_data.csv', multiple=False),
+        ],
+    )
+
+
+# Function to display a preview of the dataset
+def preview_dataset(q):
+    del q.page['upload_dataset']
+    del q.page['plot_view']
+    del q.page['education_plot']
+    del q.page['occupation_plot']
+    del q.page['age_plot']
+    del q.page['target_vs_age_plot']
+    del q.page['target_vs_hours_plot']
+    del q.page['preprocessing_data']
+    del q.page['train_model']
+    if df is None:
+        q.page['preview_dataset'] = ui.form_card(
+            box='1 3 12 7',
+            items=[
+                ui.text_l('**PREVIEW DATASET**'),
+                ui.text('Please upload the dataset first.'),
+            ],
+        )
+        return
+    q.page['preview_dataset'] = ui.form_card(
+        box='1 3 12 7',
+        items=[
+            ui.text_l('**PREVIEW DATASET**'),
+            ui.text(make_markdown_table(fields=df.columns.tolist(), rows=df.head(5000).values.tolist()))
+        ],
+    )
+
+
+# Function to plot data distribution for specific columns
+def plot_view(q):
+    del q.page['upload_dataset']
+    del q.page['preview_dataset']
+    del q.page['plot_view']
+    del q.page['preprocessing_data']
+    del q.page['train_model']
+    if df is None:
+        q.page['plot_view'] = ui.form_card(
+            box='1 3 12 7',
+            items=[
+                ui.text_l('**PLOT VIEW**'),
+                ui.text('Please upload the dataset first.'),
+            ],
+        )
+        return
+
+    # Data distribution for education, occupation, and age
+    education_distribution = df.groupby([' education', ' sex']).size().reset_index(name='count')
+    occupation_distribution = df.groupby([' occupation', ' sex']).size().reset_index(name='count')
+    age_distribution = df.groupby(['age', ' sex']).size().reset_index(name='count')
+    target_vs_age = df.groupby(['age', ' amount']).size().reset_index(name='count')
+    target_vs_hours = df.groupby([' hour-per-week', ' amount']).size().reset_index(name='count')
+
+    q.page['education_plot'] = ui.plot_card(
+        box='1 3 4 3',
+        title='Education Distribution',
+        data=data(fields=['education', 'sex', 'count'], rows=education_distribution.values.tolist()),
+        plot=ui.plot([
+            ui.mark(type='interval',
+                    x='=education', y='=count',
+                    color='=sex')
+        ])
+    )
+
+    q.page['occupation_plot'] = ui.plot_card(
+        box='5 3 4 3',
+        title='Occupation Distribution',
+        data=data(fields=['occupation', 'sex', 'count'], rows=occupation_distribution.values.tolist()),
+        plot=ui.plot([
+            ui.mark(type='interval',
+                    x='=occupation', y='=count',
+                    color='=sex')
+        ])
+    )
+
+    q.page['age_plot'] = ui.plot_card(
+        box='9 3 4 3',
+        title='Age Distribution',
+        data=data(fields=['age', 'sex', 'count'], rows=age_distribution.values.tolist()),
+        plot=ui.plot([ui.mark(type='line', x='=age', y='=count', color='=sex')]),
+    )
+
+    q.page['target_vs_age_plot'] = ui.plot_card(
+        box='3 6 4 3',
+        title='Amount vs Age',
+        data=data(fields=['age', 'amount', 'count'], rows=target_vs_age.values.tolist()),
+        plot=ui.plot(
+            [ui.mark(type='point', x='=age', y='=count', size='=count', color='=amount', shape='circle')]),
+    )
+
+    q.page['target_vs_hours_plot'] = ui.plot_card(
+        box='7 6 4 3',
+        title='Amount vs Hours Per Week',
+        data=data(fields=['hour-per-week', 'amount', 'count'], rows=target_vs_hours.values.tolist()),
+        plot=ui.plot([ui.mark(type='point', x='=hour-per-week', y='=amount', size='=count', color='=amount',
+                              shape='circle')]),
+    )
+
+
+# Function to preprocess the data
+def preprocess(df):
+    df = df.replace('?', pd.NA).dropna()
+    df = pd.get_dummies(df, columns=[' workclass', ' education', ' marital-status', ' occupation', ' relationship',
+                                     ' race', ' sex', ' native-country'])
+    df[' amount'] = df[' amount'].map({' <=50K': 0, ' >50K': 1})
+    df.drop(' fnlwgt', axis=1, inplace=True)
+    return df
+
+
+# Function to handle data preprocessing
+def preprocessing_data(q):
+    del q.page['upload_dataset']
+    del q.page['preview_dataset']
+    del q.page['plot_view']
+    del q.page['education_plot']
+    del q.page['occupation_plot']
+    del q.page['age_plot']
+    del q.page['target_vs_age_plot']
+    del q.page['target_vs_hours_plot']
+    del q.page['preprocessing_data']
+    del q.page['train_model']
+    global df, test_df, train, test
+
+    if df is None:
+        q.page['preprocessing_data'] = ui.form_card(
+            box='1 3 12 7',
+            items=[
+                ui.text_l('**PREPROCESS DATA**'),
+                ui.text('Please upload the dataset first.'),
+            ],
+        )
+        return
+    test_df = test_data()
+    train = preprocess(df)
+    test = preprocess(test_df)
+    train_data_table = make_preprocess_table(train)
+    print('Preprocess Uploaded Dataset')
+    print(train.head(10))
+    print('Preprocess Test Dataset')
+    print(test.head(10))
+
+    q.page['preprocessing_data'] = ui.form_card(
+        box='1 3 12 7',
+        items=[
+            ui.text_l('**PREPROCESSED DATA**'),
+            ui.text(train_data_table),
+        ],
+    )
+
+
+# Function to train model
+def random_forest_training(train, test):
+    y = train[' amount']
+    train = train.drop([' amount'], axis=1)
+    X = train.values
+    clf = RandomForestClassifier(max_depth=5, random_state=0)
+    clf.fit(X, y)
+    return clf
+
+
+# Function to get information about the trained model
+def get_model_info(clf):
+    model_info = {
+        'Feature Importances': clf.feature_importances_,
+        'Number of Estimators': clf.n_estimators,
+        'Max Depth': clf.max_depth,
+        'Number of Classes': clf.n_classes_,
+    }
+    return model_info
+
+
+# Function to train the machine learning model
+def train_model(q):
+    del q.page['upload_dataset']
+    del q.page['preview_dataset']
+    del q.page['plot_view']
+    del q.page['education_plot']
+    del q.page['occupation_plot']
+    del q.page['age_plot']
+    del q.page['target_vs_age_plot']
+    del q.page['target_vs_hours_plot']
+    del q.page['preprocessing_data']
+    del q.page['train_model']
+    global df, train, test
+
+    if df is not None:
+        q.page['train_model'] = ui.form_card(box='1 12 5 2', items=[ui.progress('Running...')])
+        model = random_forest_training(train, test)
+        model_info = get_model_info(model)
+
+        # Message to indicate successful model training
+        message = 'Model Trained Successfully'
+        field_names = list(model_info.keys())
+        rows = [list(model_info.values())]
+
+        markdown_table = make_markdown_table(fields=field_names, rows=rows)
+
+        q.page['train_model'] = ui.form_card(
+            box='1 3 12 7',
+            items=[
+                ui.text_l('**TRAIN AND PREDICT MODEL**'),
+                ui.message_bar('info', message),
+                ui.text(markdown_table),
+            ],
+        )
+    else:
+        q.page['train_model'] = ui.form_card(
+            box='1 3 12 7',
+            items=[
+                ui.text_l('**TRAIN AND PREDICT MODEL**'),
+                ui.text('Please preprocess data before training the model'),
+            ],
+        )