From 380e23b1c9af3f857c3a65ed343459b2393e66d0 Mon Sep 17 00:00:00 2001
From: Paul-Louis NECH <pln@algolia.com>
Date: Tue, 26 Nov 2019 16:50:40 +0100
Subject: [PATCH] refactor(dawa): Move unrandomize to lstm

---
 KoozDawa/dawa.py    | 10 ++--------
 KoozDawa/tweet.py   |  6 ++++--
 glossolalia/lstm.py | 23 ++++++++++++++---------
 3 files changed, 20 insertions(+), 19 deletions(-)

diff --git a/KoozDawa/dawa.py b/KoozDawa/dawa.py
index 8cb0142..11f1222 100644
--- a/KoozDawa/dawa.py
+++ b/KoozDawa/dawa.py
@@ -15,8 +15,7 @@ def train():
     dropout = .3  # TODO finetune layers/dropout
     validation_split = 0.2
     lstm = LisSansTaMaman(nb_layers, dropout, validation_split, debug=True)
-    filename_model = "../models/dawa/dawa_lstm%i-d%.1f-{epoch:02d}_%i-{accuracy:.4f}.hdf5" % (
-        nb_layers, dropout, nb_epoch)
+    filename_model = "../models/dawa/dawa_lstm%i-d%.1f-{epoch:02d}_%i-{accuracy:.4f}.hdf5" % (nb_layers, dropout, nb_epoch)
     filename_output = "./output/dawa_%i-d%.1f_%s.txt" % (
         nb_layers, dropout, datetime.now().strftime("%y%m%d_%H%M"))
     callbacks_list = [
@@ -32,12 +31,7 @@ def train():
                      callbacks=callbacks_list,
                      validation_split=validation_split)
 
-            for seed in ["", "Je", "Tu", "Le", "La", "Les", "Un", "On", "Nous"]:
-                print(lstm.predict(seed, nb_words))
-
-        # model.save(model_file)
-        # else: # FIXME: Load and predict, maybe reuse checkpoints?
-        # model = load_model(model_file)
+            print(lstm.predict_seeds(nb_words))
 
         for i, seed in enumerate(load_seeds(corpus, 5)):
             output = lstm.predict(seed, nb_words)
diff --git a/KoozDawa/tweet.py b/KoozDawa/tweet.py
index 04260c8..0799730 100644
--- a/KoozDawa/tweet.py
+++ b/KoozDawa/tweet.py
@@ -8,14 +8,16 @@ def tweet():
     # le soleil est triste
     # on a pas un martyr parce qu't'es la
     # des neiges d'insuline
-    # une hypothèse qu'engendre la haine n'est qu'une prison vide
     # Un jour de l'an commencé sur les autres
-    # Relater l'passionnel dans les casseroles d'eau de marécages
+    # une hypothèse qu'engendre la haine n'est qu'une prison vide
     # sniff de Caravage rapide
+    # Relater l'passionnel dans les casseroles d'eau de marécages
     # La nuit c'est le soleil
 
+    # Les rues d'ma vie se terminent par la cannelle
     # Les rues d'ma vie se terminent par des partouzes de ciel
     # des glaçons pour les yeux brisées
+    # je suis pas juste un verbe que t'observe
 
     Tweeper("KoozDawa").tweet("tassepés en panel")
 
diff --git a/glossolalia/lstm.py b/glossolalia/lstm.py
index e8c00b2..51dec94 100644
--- a/glossolalia/lstm.py
+++ b/glossolalia/lstm.py
@@ -15,15 +15,6 @@ warnings.filterwarnings("ignore")
 warnings.simplefilter(action='ignore', category=FutureWarning)
 
 
-def debug_unrandomize():
-    from numpy.random import seed
-    from tensorflow_core.python.framework.random_seed import set_random_seed
-
-    # set seeds for reproducibility
-    set_random_seed(2)
-    seed(1)
-
-
 class LisSansTaMaman(object):
     """ A LSTM model adapted for french lyrical texts."""
 
@@ -65,6 +56,11 @@ class LisSansTaMaman(object):
                               validation_split=validation_split,
                               epochs=epochs, initial_epoch=initial_epoch)
 
+    def predict_seeds(self, seeds: List[str] = None, nb_words=None):
+        if seeds is None:
+            seeds = ["", "Je", "Tu", "Le", "La", "Les", "Un", "On", "Nous"]
+        return [self.predict(seed, nb_words) for seed in seeds]
+
     def predict(self, seed="", nb_words=None):
         if nb_words is None:
             nb_words = 20  # TODO: Guess based on model a good number of words
@@ -115,3 +111,12 @@ def generate_text(model: Sequential, tokenizer: Tokenizer, seed_text="", nb_word
         predicted = model.predict_classes(token_list, verbose=2)[0]
         output += " " + word_indices[predicted]
     return output.capitalize()
+
+
+def debug_unrandomize():
+    from numpy.random import seed
+    from tensorflow_core.python.framework.random_seed import set_random_seed
+
+    # set seeds for reproducibility
+    set_random_seed(2)
+    seed(1)
--
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