visualize.ml_animation

gen_classification_plot(x_tensor, y_true, model=None, threshold=0.5, cnt_points=1000, k=0.1, title=None, epsilon=0.0001, insert_na=False)

Returns a graph with a distribution and an optional line. If dim(x) = 2, then you can get model. If dim(x) > 2, then returns graph of TSNE from sklearn.manifold with default settings. dim(x) is not support

Note

if model os linear and have one layer, simple activation function, then visualization will faster

Warning

if the model is heavy, then you should reduce cnt_points, but the probability of missing points is higher, and the visualization will be rather incorrect. You can increase the gap by increasing the epsilon.

Parameters:

• x_tensor (Tensor) – training tensor

• y_true (Tensor) – target tensor. array with true values of binary classification

• model (Optional[Module]) – some model that returns a torch tensor with class 1 probabilities using the call: model(x)

• threshold (float) – if model(xi) >= threshold, then yi = 1

• cnt_points (int) – number of points on each of the two axes when dim(x) = 2

• k (float) – constant for draw on section: [x.min() - (x.max() - x.min()) * k, x.max() + (x.max() - x.min()) * k]

• title (Optional[str]) – title of plots

• epsilon (float) – contour line points: \(\{x\in \mathbb{R}^2 \, | \, \text{threshold} - \text{epsilon} \le \text{model}(x) \le \text{threshold} + \text{epsilon}\}\)

• insert_na (bool) – na insertion flag when two points too far away

Returns:

scatter plot go.Figure

Return type:

Figure

>>> from sklearn.datasets import make_moons
>>> torch.random.manual_seed(7)
>>> x, y = make_moons(1000, noise=0.15, random_state=7)
>>> x, y = torch.tensor(x), torch.tensor(y)

>>> lr_rbf = LogisticRegressionRBF(x[:50])
>>> lr_rbf.fit(x, y, epochs=5000)

>>> lr_rbf.metrics_tab(x, y)

{'recall': 0.9980000257492065,
 'precision': 0.9842209219932556,
 'accuracy': 0.9909999966621399,
 'f1': 0.9910625822119956,
 'auc_roc': 0.9995800006320514}

>>> gen_classification_plot(x, y, model, threshold=0.5, epsilon=0.001)

---

roc_curve_plot(y_true, y_prob, fill=False)

Return figure with plotly.Figure ROC curve

Parameters:

• y_true (Tensor) – array with true values of binary classification

• y_prob (Tensor) – array of probabilities of confidence of belonging to the 1st class

• fill (bool) – flag for filling the area under the curve

Returns:

go.Figure

Return type:

Figure

>>> yt = torch.tensor([1, 1, 0, 0, 1, 0])
>>> yp = torch.tensor([0.7, 0.6, 0.3, 0.5, 0.4, 0.4])
>>> roc_curve_plot(yt, yp)

gen_regression_plot(x_tensor, y_tensor, model=None, title='<b>Scatter plot</b>')

Returns a graph with a regression and scatter of initial distribution.

Note

Support 1d x_tensor. If x_tensor n_d method applied t-SNE

Parameters:

• x_tensor (Tensor) – training tensor

• y_tensor (Tensor) – target tensor. array with true regression values

• model (Optional[Module]) – some model that returns a torch tensor with class 1 probabilities using the call: model(x)

• title (Optional[str]) – title of plots

Returns:

scatter plot go.Figure and line of regression

Return type:

Figure

>>> from sklearn.datasets import make_regression
>>> x, y = make_regression(200, 1, noise=20, random_state=21)
>>> x, y = torch.tensor(x), torch.tensor(y)
>>> regression = LinearRegression().fit(x, y)
>>> gen_regression_plot(x, y, regression)

>>> # Let's create 4-dimensional data and perform a linear regression.
>>> # After that, t-sne will show the data on the plane

>>> x, y = make_regression(200, 4, noise=20, random_state=21)
>>> x, y = torch.tensor(x), torch.tensor(y)
>>> regression = LinearRegression().fit(x, y)
>>> gen_regression_plot(x, y, regression)

>>> regression.metrics_tab(x, y)

{'r2': 0.9711183309555054,
 'mae': 15.044872283935547,
 'mse': 365.99530029296875,
 'mape': 55.71377182006836}