Testing improved

Author: rlaplaza

.github/workflows/github-pytest.yml

@@ -39,4 +39,4 @@ jobs:
         run: python setup.py install
 
       - name: Test with pytest
-        run: pytest
+        run: pytest navicat_spock/*py

navicat_spock/helpers.py

@@ -48,29 +48,29 @@ def call_imputer(a, b, imputer_strat="iterative"):
         return a
 
 
-def slope_check(beta_hats, verb=0):
+def slope_check(alphas, verb=0):
     if verb > 4:
-        print(f"Slopes are: {beta_hats}")
-    if beta_hats is None:
+        print(f"Slopes are: {alphas}")
+    if alphas is None:
         return False
-    if len(beta_hats) == 1:
+    if len(alphas) == 1:
         return True
-    if len(beta_hats) == 2:
-        if beta_hats[0] > 0:
-            return beta_hats[1] < 0
-        if beta_hats[0] < 0:
-            return beta_hats[1] > 0
-    if len(beta_hats) == 3:
-        if beta_hats[0] > 0:
-            if beta_hats[1] > 0:
-                return beta_hats[2] < 0
-            if beta_hats[1] < 0:
-                return beta_hats[2] < 0
-        if beta_hats[0] < 0:
-            if beta_hats[1] > 0:
-                return beta_hats[2] > 0
-            if beta_hats[1] < 0:
-                return beta_hats[2] > 0
+    if len(alphas) == 2:
+        if alphas[0] > 0:
+            return alphas[1] < 0
+        if alphas[0] < 0:
+            return alphas[1] > 0
+    if len(alphas) == 3:
+        if alphas[0] > 0:
+            if alphas[1] > 0:
+                return alphas[2] < 0
+            if alphas[1] < 0:
+                return alphas[2] < 0
+        if alphas[0] < 0:
+            if alphas[1] > 0:
+                return alphas[2] > 0
+            if alphas[1] < 0:
+                return alphas[2] > 0
     return False
 
 
@@ -489,7 +489,7 @@ def check_input(filenames, wp, imputer_strat, verb):
     return dfs
 
 
-def test_vif_1():
+def test_vif():
     sol = np.array([22.95, 3.0, 12.95, 3.0])
     a = [1, 1, 2, 3, 4]
     b = [2, 2, 3, 2, 1]
@@ -503,5 +503,13 @@ def test_vif_1():
     assert np.allclose(vif, sol)
 
 
+def test_slope_check():
+    assert not slope_check([10, 10, 10])
+    assert not slope_check([10, -10, 10])
+    assert not slope_check([-10, 10, -10])
+    assert slope_check([10, 10, -10])
+    assert slope_check([10, -10, -10])
+
+
 if __name__ == "__main__":
-    test_vif_1()
+    test_vif()

navicat_spock/piecewise_regression.py

@@ -647,18 +647,30 @@ def get_results(self):
             results["rss"] = self.best_muggeo.best_fit.residual_sum_squares
             results["converged"] = True
             breakpoints = self.best_muggeo.best_fit.next_breakpoints
-            slopes = [self.best_muggeo.best_fit.raw_params[1]]
-            for b in self.best_muggeo.best_fit.raw_params[2 : 2 + len(breakpoints)]:
-                slopes.append(b + self.best_muggeo.best_fit.raw_params[1])
-            results["betas"] = np.array(slopes)
+            # slopes = [self.best_muggeo.best_fit.raw_params[1]]
+            slopes = []
+
+            # Lets get the slopes right
+            alpha_names = [
+                "alpha{}".format(alpha_i)
+                for alpha_i in range(1, self.n_breakpoints + 1)
+            ]
+            for est_name in alpha_names:
+                slopes.append(self.estimates[est_name]["estimate"])
+
+            # for i, b in enumerate(
+            #    self.best_muggeo.best_fit.raw_params[2 : 2 + len(breakpoints)]
+            # ):
+            #    slopes.append(b + slopes[-1])
+            results["slopes"] = np.array(slopes)
         else:
             results["converged"] = False
             results["estimates"] = None
             results["bic"] = None
             results["bic_w"] = None
             results["aic_w"] = None
             results["rss"] = None
-            results["betas"] = None
+            results["slopes"] = None
         return results
 
     def bootstrap_restarting(self):
@@ -1052,8 +1064,8 @@ def summary(self):
             table_contents += single_line
 
             table_contents += (
-                "These alphas(gradients of segments) are estimated"
-                "from betas(change in gradient)\n"
+                "These alphas (gradients of segments) are estimated "
+                "from betas (change in gradient)\n"
             )
 
             alpha_names = [

navicat_spock/plotting2d.py

@@ -136,7 +136,18 @@ def plot_2d(
 
 
 def plot_and_save(
-    pw_fit, tags, idx, tidx, cb, ms, plotmode, fig, ax, return_value=True, save_fig=True, save_csv=True
+    pw_fit,
+    tags,
+    idx,
+    tidx,
+    cb,
+    ms,
+    plotmode,
+    fig,
+    ax,
+    return_value=True,
+    save_fig=True,
+    save_csv=True,
 ):
     # Try to figure out good dimensions for the axes and ticks
     x = pw_fit.xx

navicat_spock/spock.py

@@ -24,7 +24,17 @@
 
 
 def run_spock():
-    (df, wp, verb, imputer_strat, plotmode, seed, prefit, save_fig, save_csv) = processargs(sys.argv[1:])
+    (
+        df,
+        wp,
+        verb,
+        imputer_strat,
+        plotmode,
+        seed,
+        prefit,
+        save_fig,
+        save_csv,
+    ) = processargs(sys.argv[1:])
     _ = run_spock_from_args(
         df=df,
         wp=wp,
@@ -131,7 +141,7 @@ def run_spock_from_args(
             )
             all_sc[i, :] = np.array(
                 [
-                    slope_check(summary["betas"], verb)
+                    slope_check(summary["slopes"], verb)
                     for summary in msel.model_summaries
                 ],
                 dtype=bool,
@@ -298,7 +308,17 @@ def run_spock_from_args(
                 pw_fit.summary()
             # Plot the data, fit, breakpoints and confidence intervals
             fig, ax = plot_and_save(
-                pw_fit, tags, idx, tidx, cb, ms, fig=fig, ax=ax, plotmode=plotmode, save_fig=save_fig, save_csv=save_csv
+                pw_fit,
+                tags,
+                idx,
+                tidx,
+                cb,
+                ms,
+                fig=fig,
+                ax=ax,
+                plotmode=plotmode,
+                save_fig=save_fig,
+                save_csv=save_csv,
             )
             return fig, ax
         else: