joshreini1

data_collections = tru.get_data_collections()
for dc in data_collections:
    tru.set_data_collection(dc)
    data_splits = tru.get_data_splits()
    for split in data_splits:
        tru.tester.add_performance_test(
            data_split_name = split,
            metric = 'AUC',
            warn_if_less_than = 0.85,
            fail_if_less_than = 0.80

joshreini1

data_collections = tru.get_data_collections()
for dc in data_collections:
    tru.set_data_collection(dc)
    splits = tru.get_data_splits()
    for split_key in splits:
        split = f'{split_key}'
        tru.tester.add_stability_test(
                comparison_data_split_name = split,
                base_data_split_name = 'train',
                metric = 'DIFFERENCE_OF_MEAN',

joshreini1

import h5py

def load_data_for_year(year) -> pd.DataFrame:
    if year > 2016:
        filename = f'GFED4.1s_{year}_beta.hdf5'
    else:
        filename = f'GFED4.1s_{year}.hdf5'
    filepath = os.path.join(data_dir, filename)
    data = h5py.File(filepath, 'r')
    df = pd.DataFrame({

joshreini1

def get_feature_for_year_and_month(data_cache,year, month, suffix) -> pd.DataFrame:
    if year not in data_cache:
        data_cache[year] = load_data_for_year(year)
    data = data_cache[year]
    df = pd.DataFrame({
        f'burned_fraction_{suffix}': data['burned_area/{:02}/burned_fraction'.format(month)],
        f'emissions_DM_{suffix}': data['emissions/{:02}/DM'.format(month)],
        f'emissions_C_{suffix}': data['emissions/{:02}/C'.format(month)],
        f'emissions_small_fire_fraction_{suffix}': data['emissions/{:02}/small_fire_fraction'.format(month)],
        f'biosphere_NPP_{suffix}': data['biosphere/{:02}/NPP'.format(month)],

joshreini1

import plotly.express as px
px.set_mapbox_access_token(mapbox_token)
fig = px.scatter_mapbox(df_usa_2016.drop('year',axis=1), color = "month", lat="lat", lon="lon", size="burned_area", size_max=15, zoom=10)
fig.show()

joshreini1

models = {}
import time
for window_size in range(1,11):
    time_start = time.time()
    key = f'{window_size}year_window'
    print(f'Training linear model for {key}')
    models[f'linear_{key}'] = LogisticRegression(random_state=321, max_iter=1000, solver='saga').fit(data_train_x[key], data_train_y[key]>0.01)

    n_est = 20
    print(f'Training gb{n_est} model for {key}')

joshreini1

project_name = 'Fire_Party'
tru.set_environment('local')
tru.add_project(project_name, score_type='probits')
extra_data_columns = ['year']
train_split_name = 'train'
burned_fraction_th = 0.01
for window_size in range(1,11):
    key = f'{window_size}year_window'
    print(key)
    tru.add_data_collection(key)

joshreini1

class target_encoder(BaseEstimator, TransformerMixin):

    def __init__(self):
        pass

    def fit(self, X, y = None):
        return self

    def transform(self, X, y = None):
        #target encode lat and long

joshreini1

class target_encoder(BaseEstimator, TransformerMixin):

    def __init__(self):
        pass

    def fit(self, X, y = None):
        return self

    def transform(self, X, y = None):
        #target encode lat and long

joshreini1

tru.set_data_collection("data_collection")
splits = tru.get_data_splits()
for split in splits:
    tru.set_data_collection("data_collection")
    tru.set_data_split(split)
    xs = tru.get_xs()
    ys = tru.get_ys()
    tru.set_data_collection("data_collection_v2")
    ys_mean = ys.mean()
    ys_std = ys.std()