Python plot_maps示例

示例#1

data_frame = principalDf.copy()

for name, model, k in models:

    principalDf = data_frame.copy()
    model.fit(principalDf)
    labels = model.labels_
    principalDf["Clusters"] = labels
    principalDf["Koppen_labels"] = koppen_labels

    df = principalDf.copy()
    plot_map('Agglomerative', k, df, lonSeries, latSeries)

    if k == 29:
        plot_maps('Agglomerative', k, df, lonSeries, latSeries)

    principalDf['veg_type'] = mapped_dataset['veg_type']
    principalDf = principalDf.loc[~(principalDf.veg_type == '0')]
    sil_df = principalDf.drop(['Clusters', 'veg_type', 'Koppen_labels'],
                              axis=1)
    sil_pred = principalDf['Clusters']

    label_encoder = LabelEncoder()
    y = label_encoder.fit_transform(principalDf['veg_type'])
    principalDf['veg_encoded'] = y

    I1 = principalDf['veg_encoded']
    I2 = principalDf["Clusters"]
    I3 = principalDf['Koppen_labels']

示例#2

文件： Birch_model_noPca.py 项目： spatanwala/climate-clustering

data_frame = principalDf.copy()

for name, model, k in models:

    principalDf = data_frame.copy()
    model.fit(principalDf)
    labels = model.predict(principalDf)
    principalDf["Clusters"] = labels
    principalDf["Koppen_labels"] = koppen_labels

    df = principalDf.copy()
    plot_map('BIRCH', k, df, lonSeries, latSeries)

    if k == 29:
        plot_maps('BIRCH', k, df, lonSeries, latSeries)

    principalDf['veg_type'] = mapped_dataset['veg_type']
    principalDf = principalDf.loc[~(principalDf.veg_type == '0')]
    sil_df = principalDf.drop(['Clusters', 'veg_type', 'Koppen_labels'],
                              axis=1)
    sil_pred = principalDf['Clusters']

    label_encoder = LabelEncoder()
    y = label_encoder.fit_transform(principalDf['veg_type'])
    principalDf['veg_encoded'] = y

    k1 = max(principalDf['veg_encoded']) + 1
    I1 = principalDf['veg_encoded'].replace({0: k1 + 1})
    k2 = max(principalDf['Koppen_labels']) + 1
    I2 = principalDf["Clusters"]

示例#3

文件： Kmeans_noPca.py 项目： spatanwala/climate-clustering

for name, model, k in models:

    principalDf = data_frame.copy()
    model.fit(principalDf)
    labels = model.labels_
    principalDf["Clusters"] = labels
    principalDf.to_csv('/Users/bilalhussain/Downloads/Plotly CSV/kmeans.csv',
                       index=False)
    principalDf["Koppen_labels"] = koppen_labels

    df = principalDf.copy()
    plot_map('KMeans', k, df, lonSeries, latSeries)

    if k == 29:
        plot_maps('KMeans', k, df, lonSeries, latSeries)

    principalDf['veg_type'] = mapped_dataset['veg_type']
    principalDf = principalDf.loc[~(principalDf.veg_type == '0')]
    sil_df = principalDf.drop(['Clusters', 'veg_type', 'Koppen_labels'],
                              axis=1)
    sil_pred = principalDf['Clusters']

    label_encoder = LabelEncoder()
    y = label_encoder.fit_transform(principalDf['veg_type'])
    principalDf['veg_encoded'] = y

    I1 = principalDf['veg_encoded']
    I2 = principalDf["Clusters"]
    I3 = principalDf['Koppen_labels']

示例#4

文件： gm_model.py 项目： spatanwala/climate-clustering

data_frame = principalDf.copy()

for name, model, k in models:

    principalDf = data_frame.copy()
    model.fit(principalDf)
    labels = model.predict(principalDf)
    principalDf["Clusters"] = labels
    principalDf["Koppen_labels"] = koppen_labels

    df = principalDf.copy()
    plot_map('Gaussian Mixture', k, df, lonSeries, latSeries)

    if k == 29:
        plot_maps('Gaussian Mixture', k, df, lonSeries, latSeries)

    principalDf['veg_type'] = mapped_dataset['veg_type']
    principalDf = principalDf.loc[~(principalDf.veg_type == '0')]
    sil_df = principalDf.drop(['Clusters', 'veg_type', 'Koppen_labels'],
                              axis=1)
    sil_pred = principalDf['Clusters']

    label_encoder = LabelEncoder()
    y = label_encoder.fit_transform(principalDf['veg_type'])
    principalDf['veg_encoded'] = y

    k1 = max(principalDf['veg_encoded']) + 1
    I1 = principalDf['veg_encoded'].replace({0: k1 + 1})
    k2 = max(principalDf['Koppen_labels']) + 1
    I2 = principalDf["Clusters"]

示例#5

data_frame = principalDf.copy()

for name, model, k in models:

    principalDf = data_frame.copy()
    model.fit(principalDf)
    labels = model.predict(principalDf)
    principalDf["Clusters"] = labels
    principalDf["Koppen_labels"] = koppen_labels

    df = principalDf.copy()
    plot_map('MinBKmeans', k, df, lonSeries, latSeries)

    if k == 29:
        plot_maps('MinBKmeans', k, df, lonSeries, latSeries)

    principalDf['veg_type'] = mapped_dataset['veg_type']
    principalDf = principalDf.loc[~(principalDf.veg_type == '0')]
    sil_df = principalDf.drop(['Clusters', 'veg_type', 'Koppen_labels'],
                              axis=1)
    sil_pred = principalDf['Clusters']

    label_encoder = LabelEncoder()
    y = label_encoder.fit_transform(principalDf['veg_type'])
    principalDf['veg_encoded'] = y

    k1 = max(principalDf['veg_encoded']) + 1
    I1 = principalDf['veg_encoded'].replace({0: k1 + 1})
    k2 = max(principalDf['Koppen_labels']) + 1
    I2 = principalDf["Clusters"]