Merge pull request #101 from lanl/develop

Develop

CITATION.cff

@@ -20,7 +20,7 @@ authors:
   - family-names: Alexandrov
     given-names: Boian
 title: "Tensor Extraction of Latent Features (T-ELF)"
-version: 0.0.11
+version: 0.0.12
 url: https://github.com/lanl/T-ELF
 doi: 10.5281/zenodo.10257897
 date-released: 2023-12-04

README.md

@@ -156,7 +156,7 @@ If you use T-ELF please cite.
 
 **APA:**
 ```latex
-Eren, M., Solovyev, N., Barron, R., Bhattarai, M., Truong, D., Boureima, I., Skau, E., Rasmussen, K., & Alexandrov, B. (2023). Tensor Extraction of Latent Features (T-ELF) (Version 0.0.11) [Computer software]. https://doi.org/10.5281/zenodo.10257897
+Eren, M., Solovyev, N., Barron, R., Bhattarai, M., Truong, D., Boureima, I., Skau, E., Rasmussen, K., & Alexandrov, B. (2023). Tensor Extraction of Latent Features (T-ELF) (Version 0.0.12) [Computer software]. https://doi.org/10.5281/zenodo.10257897
 ```
 
 **BibTeX:**

TELF/factorization/NMFk.py

@@ -260,9 +260,10 @@ def _nmf_parallel_wrapper(
 
     #
     # cluster the solutions
-    #        
+    # 
     W, W_clust = custom_k_means(W_all, use_gpu=False)
-    sils_all = silhouettes(W_clust)
+    sils_all_W = silhouettes(W_clust)
+    sils_all_H = silhouettes(np.array(H_all).transpose((1, 0, 2)))
 
     #
     # concensus matrix
@@ -320,7 +321,8 @@ def _nmf_parallel_wrapper(
         save_data = {
             "W": W,
             "H": H,
-            "sils_all": sils_all,
+            "sils_all_W": sils_all_W,
+            "sils_all_H": sils_all_H,
             "error_reg": error_reg,
             "errors": errors,
             "reordered_con_mat": reordered_con_mat,
@@ -341,12 +343,18 @@ def _nmf_parallel_wrapper(
         for key in logging_stats:
             if key == 'k':
                 plot_data["k"] = k
-            elif key ==  'sils_min':
-                sils_min = np.min(np.mean(sils_all, 1))
-                plot_data["sils_min"] = '{0:.3f}'.format(sils_min)
-            elif key == 'sils_mean':
-                sils_mean = np.mean(np.mean(sils_all, 1))
-                plot_data["sils_mean"] = '{0:.3f}'.format(sils_mean)
+            elif key ==  'sils_min_W':
+                sils_min_W = np.min(np.mean(sils_all_W, 1))
+                plot_data["sils_min_W"] = '{0:.3f}'.format(sils_min_W)
+            elif key == 'sils_mean_W':
+                sils_mean_W = np.mean(np.mean(sils_all_W, 1))
+                plot_data["sils_mean_W"] = '{0:.3f}'.format(sils_mean_W)
+            elif key ==  'sils_min_H':
+                sils_min_H = np.min(np.mean(sils_all_H, 1))
+                plot_data["sils_min_H"] = '{0:.3f}'.format(sils_min_H)
+            elif key == 'sils_mean_H':
+                sils_mean_H = np.mean(np.mean(sils_all_H, 1))
+                plot_data["sils_mean_H"] = '{0:.3f}'.format(sils_mean_H)
             elif key == 'err_mean':
                 err_mean = np.mean(errors)
                 plot_data["err_mean"] = '{0:.3f}'.format(err_mean)
@@ -373,10 +381,17 @@ def _nmf_parallel_wrapper(
         "err_mean":np.mean(errors),
         "err_std":np.std(errors),
         "err_reg":error_reg,
-        "sils_min":np.min(np.mean(sils_all, 1)),
-        "sils_mean":np.mean(np.mean(sils_all, 1)),
-        "sils_std":np.std(np.mean(sils_all, 1)),
-        "sils_all":sils_all,
+
+        "sils_min_W":np.min(np.mean(sils_all_W, 1)),
+        "sils_mean_W":np.mean(np.mean(sils_all_W, 1)),
+        "sils_std_W":np.std(np.mean(sils_all_W, 1)),
+        "sils_all_W":sils_all_W,
+
+        "sils_min_H":np.min(np.mean(sils_all_H, 1)),
+        "sils_mean_H":np.mean(np.mean(sils_all_H, 1)),
+        "sils_std_H":np.std(np.mean(sils_all_H, 1)),
+        "sils_all_H":sils_all_H,
+
         "cophenetic_coeff":coeff_k,
         "col_err":curr_col_err,
     }
@@ -404,7 +419,7 @@ def __init__(
             save_output=True,
             collect_output=False,
             predict_k=False,
-            predict_k_method="pvalue",
+            predict_k_method="sill",
             verbose=True,
             nmf_verbose=False,
             perturb_verbose=False,
@@ -462,13 +477,13 @@ def __init__(
                 Even when ``predict_k=False``, number of latent factors can be estimated using the figures saved in ``save_path``.
 
         predict_k_method : str, optional
-            Method to use when performing automatic k prediction. Default is "pvalue".\n
+            Method to use when performing automatic k prediction. Default is "sill".\n
             * ``predict_k_method='pvalue'`` will use L-Statistics with column-wise error for automatically estimating the number of latent factors.\n
             * ``predict_k_method='sill'`` will use Silhouette score for estimating the number of latent factors.
 
             .. warning::
 
-                ``predict_k_method='pvalue'`` prediction will result in significantly longer processing time! ``predict_k_method='sill'``, on the other hand, will be much faster.
+                ``predict_k_method='pvalue'`` prediction will result in significantly longer processing time, altough it is more accurate! ``predict_k_method='sill'``, on the other hand, will be much faster.
 
         verbose : bool, optional
             If True, shows progress in each k. The default is True.
@@ -789,8 +804,11 @@ def fit(self, X, Ks, name="NMFk", note=""):
         # init the stats header 
         # this will setup the logging for all configurations of nmfk
         stats_header = {'k': 'k', 
-                        'sils_min': 'Min. Silhouette', 
-                        'sils_mean': 'Mean Silhouette'}
+                        'sils_min_W': 'W Min. Silhouette', 
+                        'sils_mean_W': 'W Mean Silhouette',
+                        'sils_min_H': 'H Min. Silhouette', 
+                        'sils_mean_H': 'H Mean Silhouette',
+                        }
         if self.calculate_error:
             stats_header['err_mean'] = 'Mean Error'
             stats_header['err_std'] = 'STD Error'
@@ -950,11 +968,14 @@ def fit(self, X, Ks, name="NMFk", note=""):
             if self.predict_k:
                 if self.predict_k_method == "pvalue":
                     k_predict = pvalue_analysis(
-                        combined_result["col_err"], Ks, combined_result["sils_min"], SILL_thr=self.sill_thresh
+                        combined_result["col_err"], Ks, combined_result["sils_min_W"], SILL_thr=self.sill_thresh
                     )[0]
                 elif self.predict_k_method == "sill":
-                    k_predict = Ks[np.max(np.argwhere(
-                        np.array(combined_result["sils_min"]) >= self.sill_thresh).flatten())]
+                    k_predict_W = Ks[np.max(np.argwhere(
+                        np.array(combined_result["sils_min_W"]) >= self.sill_thresh).flatten())]
+                    k_predict_H = Ks[np.max(np.argwhere(
+                        np.array(combined_result["sils_min_H"]) >= self.sill_thresh).flatten())]
+                    k_predict = min(k_predict_W, k_predict_H)
             else:
                 k_predict = 0
 
@@ -998,7 +1019,8 @@ def fit(self, X, Ks, name="NMFk", note=""):
                     self.save_path_full, 
                     plot_predict=self.predict_k,
                     plot_final=True,
-                    simple_plot=self.simple_plot
+                    simple_plot=self.simple_plot,
+                    calculate_error=self.calculate_error
                 )
                 append_to_note(["#" * 100], self.save_path_full, name=note_name, lock=self.lock)
                 append_to_note(["end_time= "+str(datetime.now())], self.save_path_full, name=note_name, lock=self.lock)

TELF/factorization/utilities/plot_NMFk.py

@@ -113,7 +113,7 @@ def plot_BNMFk(Ks, sils, bool_err, path=None, name=None):
     return None
 
 
-def plot_NMFk(data, k_predict, name, path, plot_predict=False, plot_final=False, simple_plot=False):
+def plot_NMFk(data, k_predict, name, path, plot_predict=False, plot_final=False, simple_plot=False, calculate_error=True):
     """
 
 
@@ -144,33 +144,52 @@ def plot_NMFk(data, k_predict, name, path, plot_predict=False, plot_final=False,
     ax1.set_xlabel("latent dimension")
 
     if pac:
-        ax1.set_ylabel("silhouette - pac", color=color)
+        ax1.set_ylabel("silhouette - pac", color="black")
     else:
         ax1.set_ylabel("silhouette", color=color)
-
+
+    # W sill  
     ax1.plot(
         list(data["Ks"]),
-        data["sils_min"],
+        data["sils_min_W"],
         "o-",
         color=color,
-        label="minimum silhouette",
+        label="W minimum silhouette",
+    )
+
+    # H sill
+    ax1.plot(
+        list(data["Ks"]),
+        data["sils_min_H"],
+        "o-.",
+        color="purple",
+        label="H minimum silhouette",
     )
 
     # add a vertical line for xtick k-values to make it easier to see which point corresponds to which k
-    if not isinstance(data["sils_min"], np.float64):  # if plot contains more than one k-value
+    if not isinstance(data["sils_min_W"], np.float64):  # if plot contains more than one k-value
         for xtick in ax1.get_xticks():
             if xtick in data["Ks"]:
-                y = data["sils_min"][np.where(data["Ks"] == xtick)[0][0]]  # get the y value that corresponds to xtick
-                plt.vlines(xtick, min(data["sils_min"] + [0]), y, colors='black', alpha=0.4)
+                y = data["sils_min_W"][np.where(data["Ks"] == xtick)[0][0]]  # get the y value that corresponds to xtick
+                plt.vlines(xtick, min([0, np.min(data["sils_min_H"]), np.min(data["sils_min_W"])]), y, colors='black', alpha=0.4)
 
     if not simple_plot:
         ax1.errorbar(
             list(data["Ks"]),
-            data["sils_mean"],
-            yerr=data["sils_std"],
+            data["sils_mean_W"],
+            yerr=data["sils_std_W"],
             fmt="^:",
             color="tab:green",
-            label=r"mean +- std silhouette",
+            label=r"W mean +- std silhouette",
+        )
+
+        ax1.errorbar(
+            list(data["Ks"]),
+            data["sils_mean_H"],
+            yerr=data["sils_std_H"],
+            fmt="^-.",
+            color="tab:green",
+            label=r"H mean +- std silhouette",
         )
 
     # pac
@@ -184,9 +203,10 @@ def plot_NMFk(data, k_predict, name, path, plot_predict=False, plot_final=False,
             color=color,
             label="PAC",
         )
-        ax1.set_ylim(min(0, min(np.min(data["sils_min"]), np.min(data["pac"]))), 1)
+
+        ax1.set_ylim(min([0, np.min(data["sils_min_H"]), np.min(data["sils_min_W"]), np.min(data["pac"])]), 1)
     else:
-        ax1.set_ylim(min(0, np.min(data["sils_min"])), 1)
+        ax1.set_ylim(min([0, np.min(data["sils_min_H"]), np.min(data["sils_min_W"])]), 1)
 
     ax1.tick_params(axis="y", labelcolor=color)
 
@@ -209,35 +229,47 @@ def plot_NMFk(data, k_predict, name, path, plot_predict=False, plot_final=False,
     )
 
     # relative error
-    ax2 = ax1.twinx()
-    color = "tab:blue"
-    ax2.set_ylabel("relative error", color=color)
-    ax2.plot(
-        list(data["Ks"]),
-        data["err_reg"],
-        "o-",
-        color=color,
-        label="regression relative error",
-    )
-
-    if not simple_plot:
-        ax2.errorbar(
+    if calculate_error:
+        ax2 = ax1.twinx()
+        color = "tab:blue"
+        ax2.set_ylabel("relative error", color=color)
+        ax2.plot(
             list(data["Ks"]),
-            data["err_mean"],
-            yerr=data["err_std"],
-            fmt="^:",
-            color="tab:orange",
-            label="perturbation relative error mean +- std",
+            data["err_reg"],
+            "o-",
+            color=color,
+            label="regression relative error",
         )
+
+        if not simple_plot:
+            ax2.errorbar(
+                list(data["Ks"]),
+                data["err_mean"],
+                yerr=data["err_std"],
+                fmt="^:",
+                color="tab:orange",
+                label="perturbation relative error mean +- std",
+            )
 
-    ax2.tick_params(axis="y", labelcolor=color)
-    ax2.legend(
+        ax2.tick_params(axis="y", labelcolor=color)
+        ax2.legend(
+            loc="upper right",
+            bbox_to_anchor=(0.5, -0.07),
+            fancybox=True,
+            shadow=True,
+            handlelength=4,
+        )
+
+    ax1.legend(
         loc="upper right",
-        bbox_to_anchor=(0.5, -0.07),
+        bbox_to_anchor=(.9, -0.07),
         fancybox=True,
         shadow=True,
+        handlelength=4,
     )
 
+
+
     # finalize
     fig.tight_layout()
     plt.title(name + "  " + str(min(list(data["Ks"]))) + "-" + str(max(list(data["Ks"]))))

TELF/version.py

@@ -1 +1 @@
-__version__ = '0.0.11'
+__version__ = '0.0.12'

docs/Beaver.html

@@ -8,7 +8,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
-    <title>TELF.pre_processing.Beaver: Fast matrix and tensor building tool &#8212; TELF 0.0.11 documentation</title>
+    <title>TELF.pre_processing.Beaver: Fast matrix and tensor building tool &#8212; TELF 0.0.12 documentation</title>
 
 
 
@@ -37,7 +37,7 @@
 <link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=365ca57ee442770a23c6" />
   <script src="_static/vendor/fontawesome/6.1.2/js/all.min.js?digest=365ca57ee442770a23c6"></script>
 
-    <script src="_static/documentation_options.js?v=2fb9ae3b"></script>
+    <script src="_static/documentation_options.js?v=38cd2e5d"></script>
     <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/scripts/sphinx-book-theme.js?digest=5a5c038af52cf7bc1a1ec88eea08e6366ee68824"></script>
@@ -127,7 +127,7 @@
 
 
 
-    <p class="title logo__title">TELF 0.0.11 documentation</p>
+    <p class="title logo__title">TELF 0.0.12 documentation</p>
 
 </a></div>
         <div class="sidebar-primary-item"><nav class="bd-links" id="bd-docs-nav" aria-label="Main">

docs/Cheetah.html

@@ -8,7 +8,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
-    <title>TELF.applications.Cheetah: Advanced search by keywords and phrases &#8212; TELF 0.0.11 documentation</title>
+    <title>TELF.applications.Cheetah: Advanced search by keywords and phrases &#8212; TELF 0.0.12 documentation</title>
 
 
 
@@ -37,7 +37,7 @@
 <link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=365ca57ee442770a23c6" />
   <script src="_static/vendor/fontawesome/6.1.2/js/all.min.js?digest=365ca57ee442770a23c6"></script>
 
-    <script src="_static/documentation_options.js?v=2fb9ae3b"></script>
+    <script src="_static/documentation_options.js?v=38cd2e5d"></script>
     <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/scripts/sphinx-book-theme.js?digest=5a5c038af52cf7bc1a1ec88eea08e6366ee68824"></script>
@@ -127,7 +127,7 @@
 
 
 
-    <p class="title logo__title">TELF 0.0.11 documentation</p>
+    <p class="title logo__title">TELF 0.0.12 documentation</p>
 
 </a></div>
         <div class="sidebar-primary-item"><nav class="bd-links" id="bd-docs-nav" aria-label="Main">