def test_do_pca(get_iris_pca):
df_iris = pd.read_csv('./tests/iris.csv',
index_col=False).drop('Unnamed: 0', axis=1)
pcomp = get_iris_pca
x_pca, pipe = viz.do_pca(
df_iris.
loc[:,
['sepal length', 'sepal width', 'petal length', 'petal width']], 2)
assert np.allclose(x_pca, pcomp, atol=0.000001)
def test_generate_pca_report(get_iris_pca):
df_iris = pd.read_csv('./tests/iris.csv',
index_col=False).drop('Unnamed: 0', axis=1)
x_pca, pipe = viz.do_pca(
df_iris.
loc[:,
['sepal length', 'sepal width', 'petal length', 'petal width']], 2)
pcomp = viz.generate_pca_report(pipe.named_steps['pca'])
df_res = pd.DataFrame(
{
"C1": [0.522372, -0.263355, 0.581254, 0.565611],
"C2": [0.372318, 0.925556, 0.021095, 0.065416]
},
index=[0, 1, 2, 3])
assert np.allclose(pcomp, df_res, atol=0.000001)
def test_table_similar_with_names(get_sample_geodata):
x, y, target_names = viz.get_pca_data(get_sample_geodata, 2018, 5)
x_pca, pipe = viz.do_pca(x, 5)
res = viz.table_similar_with_names(get_sample_geodata,
"Vattuniemi",
["Lauttasaari", "Ruoholahti - Kamppi"],
target_names,
x_pca,
cols=["he_kika"])
df_res = pd.DataFrame(
{
"nimi": ['Vattuniemi', 'Lauttasaari'],
"he_kika": [42.0, 40.0],
"dist": [0.0, 4.573346]
},
index=[16, 11])
assert np.allclose(res.loc[:, ["he_kika", "dist"]],
df_res.loc[:, ["he_kika", "dist"]],
atol=0.000001)
assert np.all(res.nimi == df_res.nimi)
def test_get_similar_in_geo_area(get_sample_geodata):
included_area = get_sample_geodata.loc[get_sample_geodata.pono.isin(
['00180', '00200', '00210'])]
x, y, target_names = viz.get_pca_data(get_sample_geodata, 2018, 5)
target_names.index = range(len(target_names))
x_pca, pipe = viz.do_pca(x, 5)
d = sim.pairwise_distances(x_pca, x_pca, 'euclidean')
res = sim.get_similar_in_geo_area(included_area,
orig_name="Vattuniemi",
d=d,
target_names=target_names,
n_most=1)
assert res == ['Lauttasaari']
included_area2 = get_sample_geodata.loc[get_sample_geodata.pono.isin(
['00180', '00210'])]
assert sim.get_similar_in_geo_area(included_area2,
orig_name="Vattuniemi",
d=d,
target_names=target_names,
n_most=1) == ["Kamppi - Ruoholahti"]
'hinta': 'int64',
'yliopistot': 'int64',
'amk': 'int64'})
df.drop(labels='Unnamed: 0', axis=1, inplace=True)
df = map_fi_plot.merge_to_polygons_for_year(df, 2018)
# df.drop(labels=['vuosi_y', 'nimi_y'], axis=1, inplace=True)
df = df.rename(index=str, columns={'vuosi_y': 'vuosi', 'nimi_y': 'nimi'})
# get uni data
# amk, yl = data_transforms.get_edu_data()
# df['yliopistot'] = [yl[x] if x in yl else 0 for x in df.pono]
# df['amk'] = [amk[x] if x in amk else 0 for x in df.pono]
X, y, target_names = viz.get_pca_data(df, 2018, 5)
target_names.index = range(len(target_names))
X_pca, pipe = viz.do_pca(X, 5)
# plot_data4 = make_graph_data(df)
# with open('plotly_plot_data4', 'wb') as f:
# pickle.dump(plot_data4, f)
with open('plotly_plot_data4', 'rb') as f:
data_pickle = pickle.load(f)
pono_name_dict = dict(zip(df.sort_values(by='pono').pono + ' ' + df.sort_values(by='pono').nimi,
df.sort_values(by='pono').nimi))
app.layout = html.Div([
html.Div([
html.Div([
html.P('Select origin area:', style={'display': 'inline-block'}),
dcc.Dropdown(