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How to use test_sizing method in Molotov

Best Python code snippet using molotov_python

test_TextureAtlas.py

1# author: Zach Crabtree zacharyc@alleninstitute.org2import unittest3from aicsimageio import AICSImage4from aicsimageprocessing.textureAtlas import generate_texture_atlas5class TestTextureAtlas(unittest.TestCase):6    def test_Save(self):7        image = AICSImage("aicsimageprocessing/tests/img/img40_1.ome.tif")8        atlas = generate_texture_atlas(9            im=image,10            pack_order=[[0, 1, 2], [3], [4]],11            name="test_Sizing",12            max_edge=1024,13        )14        atlas.save("img/atlas_max")15    def test_Sizing(self):16        # arrange17        image = AICSImage("aicsimageprocessing/tests/img/img40_1.ome.tif")18        max_edge = 204819        # act20        atlas = generate_texture_atlas(21            im=image,22            name="test_Sizing",23            max_edge=max_edge,24            pack_order=[[3, 2, 1, 0], [4]],25        )26        atlas_maxedge = max(atlas.dims.atlas_width, atlas.dims.atlas_height)27        # assert28        self.assertTrue(atlas_maxedge <= max_edge)29    def test_pickChannels(self):30        packing_list = [[0], [1, 2], [3, 4]]31        # arrange32        image = AICSImage("aicsimageprocessing/tests/img/img40_1.ome.tif")33        # act34        atlas = generate_texture_atlas(35            image, name="test_pickChannels", pack_order=packing_list36        )37        # returns as dict38        # metadata = atlas.get_metadata()39        # returns list of dicts40        image_dicts = atlas.atlas_list41        output_packed = [img.metadata["channels"] for img in image_dicts]42        # assert43        self.assertEqual(packing_list, output_packed)44    def test_metadata(self):45        packing_list = [[0], [1, 2], [3, 4]]46        prefix = "atlas"47        # arrange48        image = AICSImage("aicsimageprocessing/tests/img/img40_1.ome.tif")49        # act50        atlas = generate_texture_atlas(51            image, name=prefix, pack_order=packing_list52        )53        # assert54        metadata = atlas.get_metadata()55        self.assertTrue(56            all(57                key in metadata58                for key in (59                    "tile_width",60                    "tile_height",61                    "width",62                    "height",63                    "channels",64                    "channel_names",65                    "tiles",66                    "rows",67                    "cols",68                    "atlas_width",69                    "atlas_height",70                    "images",71                )72            )73        )...

supervisedpolynomial.py

Source: supervisedpolynomial.py

1import pandas as pd 2import numpy as np3import matplotlib.pyplot as plt4from sklearn.model_selection import train_test_split5from sklearn.preprocessing import PolynomialFeatures6from sklearn.linear_model import LinearRegression7from sklearn.pipeline import Pipeline8from sklearn.model_selection import GridSearchCV910class Polynomialfit():11    12    def __init__(self, data_frame, low_degree, high_degree, x_curve_start, x_curve_stop, linspace_num=50, test_size = 0.2, random_state = None):13        self.data_frame = data_frame14        self.low_degree = low_degree15        self.high_degree = high_degree16        self.dataFrameHandler(self.data_frame)17        self.stratifiedHandler(test_size, random_state)18        self.polynomialEstimatorHandler()19        self.X_curve = self.getXCurve(x_curve_start, x_curve_stop, linspace_num)20        self.y_curve = self.getYCurve()21        22    def dataFrameHandler(self, data_frame):23        self.X = data_frame.values[:,0].reshape(-1,1)24        self.y = data_frame.values[:,1]25        26    def stratifiedHandler(self, test_sizing, rdm_state):27        bins = np.round(self.X)28        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.X,self.y,test_size = test_sizing, stratify = bins, random_state=rdm_state)29        30    def polynomialEstimatorHandler(self):31        pipeline = Pipeline((32            ("poly_features", PolynomialFeatures(degree=2)),33            ("lin_reg", LinearRegression(fit_intercept=False)),))3435        degrees = range(1,20)36        parameters = {"poly_features__degree": degrees}3738        grid_search = GridSearchCV(pipeline, parameters, cv=5)3940        # Fit GridSearchCV object41        grid_search.fit(self.X_train, self.y_train)42        43        # extract the selected model44        self.best_model = grid_search.best_estimator_45        46    def getXCurve(self, x_curve_start, x_curve_stop, linspace_num):47        return np.linspace(x_curve_start, x_curve_stop, linspace_num).reshape(-1, 1)48    49    def getYCurve(self):50        return self.best_model.predict(self.X_curve)51            52    def plot(self):53        plt.title('Polynomial fit')54        plt.xlabel('Feature')55        plt.ylabel('Target value')        56        plt.plot(self.X_train, self.y_train, 'o', label='Training Data')57        plt.plot(self.X_test, self.y_test, 'o', label='Test Data')58        plt.plot(self.X_curve, self.y_curve, 'r')59        plt.legend()
...

test_pairs.py

Source: test_pairs.py

1from math import floor2from twcli.pairs import sizing3def test_sizing():4    """Apply test cases for pairs sizing"""5    args = dict(price_left=100, price_right=25, vol_left=2, vol_right=7,6                unit_size_left=100, multiplier_left=1, multiplier_right=1)7    def msg():8        return f"Expected {exp:.2f} units, but calculated {act:.2f}."9    pair_specs = sizing.size(**args)10    exp = 11411    act = floor(pair_specs['unit_size_right'])12    assert act == exp, msg()13    args['multiplier_left'] = 10014    args['multiplier_right'] = 1015    pair_specs = sizing.size(**args)16    exp = 114217    act = floor(pair_specs['unit_size_right'])...

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