adding skeleton of eval scripts

src/scripts/evaluate.py

@@ -5,6 +5,15 @@
 
 import argparse
 import pickle
+from sbi.analysis import pairplot
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+
+# plotting style things:
+# remove top and right axis from plots
+matplotlib.rcParams["axes.spines.right"] = False
+matplotlib.rcParams["axes.spines.top"] = False
 
 
 class InferenceModel:
@@ -36,7 +45,6 @@ def load_model_pkl(self, path, model_name):
     def infer_sbi(self, posterior, n_samples, y_true):
         return posterior.sample((n_samples,), x=y_true)
 
-
     def predict(input, model):
         """
 
@@ -45,6 +53,77 @@ def predict(input, model):
         :return: Prediction
         """
         return 0
+
+    def simulator()
+
+
+class Display:
+    def mackelab_corner_plot(self,
+                        posterior_samples,
+                        labels_list=None,
+                        limit_list=None,
+                        truth_list=None):
+        """
+        Uses existing pairplot from mackelab analysis to produce a flexible
+        corner plot.
+
+        :param posterior_samples: Samples drawn from the posterior, conditional on data
+        :param labels_list: A list of the labels for the parameters
+        :param limit_list: A list of limits for each parameter plot
+        :return: Loaded model object that can be used with the predict function
+        """
+        # plot posterior samples
+        #if labels_list:
+        #if limit_list:
+        fig, axes = pairplot(
+            posterior_samples,
+            labels_list=labels_list,
+            limits=limit_list,
+            #[[0,10],[-10,10],[0,10]],
+            truths=truth_list,
+            figsize=(5, 5)
+        )
+        axes[0, 1].plot([truth_list[1]], [truth_list[0]], marker="o", color="red")
+    def improved_corner_plot(self, posterior, params):
+        """
+        Improved corner plot
+        """
+
+
+class Diagnose:
+    def posterior_predictive(self, theta_true, x_true, simulator, posterior_samples):
+        # not sure how or where to define the simulator
+        # could require that people input posterior predictive samples,
+        # already drawn from the simulator
+        posterior_predictive_samples = simulator(posterior_samples)
+        y_true = simulator(theta_true, x_true)
+        # also go through and plot one sigma interval
+        # plot the true values
+        plt.clf()
+        xs_sim = np.linspace(0, 100, 101)
+        ys_sim = np.array(posterior_predictive_samples)
+        plt.fill_between(xs_sim,
+                        np.mean(ys_sim, axis = 0) - 1 * np.std(ys_sim, axis = 0),
+                        np.mean(ys_sim, axis = 0) + 1 * np.std(ys_sim, axis = 0),
+                        color = '#FF495C', label = 'posterior predictive check with noise')
+        plt.plot(xs_sim, np.mean(ys_sim, axis = 0) + true_sigma,
+                color = '#25283D', label = 'true input error')
+        plt.plot(xs_sim, np.mean(ys_sim, axis = 0) - true_sigma,
+                color = '#25283D')
+        plt.scatter(xs_sim,
+                    np.array(y_true), 
+                    color = 'black')#'#EFD9CE')  
+
+        plt.legend()
+        plt.show()
+        return ys_sim
+
+    def sbc_mackelab(self, posterior, params):
+        """
+        Runs and displays mackelab's SBC (simulation-based calibration)
+        """
+
+
 
 
 if __name__ == '__main__':