Published new version

keras_bed_sequence.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: keras-bed-sequence
-Version: 1.1.2
+Version: 1.1.3
 Summary: Lazily one-hot encoding bed sequences using Keras Sequence.
 Home-page: https://github.com/LucaCappelletti94/keras_bed_sequence
 Author: Luca Cappelletti
@@ -28,195 +28,6 @@ Description: keras_bed_sequence
 
         |coveralls| |sonar_coverage| |code_climate_coverage|
 
-        Usage examples
-        ------------------------
-        The following examples are tested within the package test suite.
-
-        Classification task example
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        Let's start by building an extremely simple classification task model:
-
-        .. code:: python
-
-            from tensorflow.keras.models import Sequential
-            from tensorflow.keras.layers import Dense, Flatten
-            from keras_mixed_sequence import MixedSequence
-
-            model = Sequential([
-                Flatten(),
-                Dense(1)
-            ])
-            model.compile(
-                optimizer="nadam",
-                loss="MSE"
-            )
-
-        We then proceed to load the training data into Keras Sequences,
-        using, in particular, a MixedSequence object:
-
-        .. code:: python
-
-            import numpy as np
-            from keras_mixed_sequence import MixedSequence
-            from keras_bed_sequence import BedSequence
-
-            batch_size = 32
-            bed_sequence = BedSequence(
-                "hg19",
-                "path/to/bed/files.bed",
-                batch_size
-            )
-            y = the_output_values
-            mixed_sequence = MixedSequence(
-                x=bed_sequence,
-                y=y,
-                batch_size=batch_size
-            )
-
-        Finally, we can proceed to use the obtained MixedSequence
-        to train our model:
-
-        .. code:: python
-
-            model.fit(
-                mixed_sequence,
-                steps_per_epoch=mixed_sequence.steps_per_epoch,
-                epochs=2,
-                verbose=0,
-                shuffle=True
-            )
-
-        Auto-encoding task example
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        Let's start by building an extremely simple auto-encoding task model:
-
-        .. code:: python
-
-            from tensorflow.keras.models import Sequential
-            from tensorflow.keras.layers import Conv2D, Reshape, Conv2DTranspose
-
-            model = Sequential([
-                Reshape((200, 4, 1)),
-                Conv2D(16, kernel_size=3, activation="relu"),
-                Conv2DTranspose(1, kernel_size=3, activation="relu"),
-                Reshape((-1, 200, 4))
-            ])
-            model.compile(
-                optimizer="nadam",
-                loss="MSE"
-            )
-
-        We then proceed to load the training data into Keras Sequences,
-        using, in particular, a MixedSequence object:
-
-        .. code:: python
-
-            import numpy as np
-            from keras_mixed_sequence import MixedSequence
-            from keras_bed_sequence import BedSequence
-
-            batch_size = 32
-            bed_sequence = BedSequence(
-                Genome("hg19", chromosomes=["chr1"]),
-                "path/to/bed/files.bed",
-                batch_size
-            )
-            mixed_sequence = MixedSequence(
-                x=bed_sequence,
-                y=bed_sequence,
-                batch_size=batch_size
-            )
-
-        Finally, we can proceed to use the obtained MixedSequence
-        to train our model:
-
-        .. code:: python
-
-            model.fit(
-                mixed_sequence,
-                steps_per_epoch=mixed_sequence.steps_per_epoch,
-                epochs=2,
-                verbose=0,
-                shuffle=True
-            )
-
-        Multi-task example (classification + auto-encoding)
-        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-        Let's start by building an extremely simple multi-tasks model:
-
-        .. code:: python
-
-            from tensorflow.keras.models import Model
-            from tensorflow.keras.layers import Dense, Conv2D, Reshape, Flatten, Conv2DTranspose, Input
-
-            inputs = Input(shape=(200, 4))
-
-            flattened = Flatten()(inputs)
-
-            output1 = Dense(
-                units=1,
-                activation="relu",
-                name="output1"
-            )(flattened)
-
-            hidden = Reshape((200, 4, 1))(inputs)
-            hidden = Conv2D(16, kernel_size=3, activation="relu")(hidden)
-            hidden = Conv2DTranspose(1, kernel_size=3, activation="relu")(hidden)
-            output2 = Reshape((200, 4), name="output2")(hidden)
-
-            model = Model(
-                inputs=inputs,
-                outputs=[output1, output2],
-                name="my_model"
-            )
-
-            model.compile(
-                optimizer="nadam",
-                loss="MSE"
-            )
-
-        We then proceed to load the training data into Keras Sequences,
-        using, in particular, a MixedSequence object:
-
-        .. code:: python
-
-            import numpy as np
-            from keras_mixed_sequence import MixedSequence
-            from keras_bed_sequence import BedSequence
-
-            batch_size = 32
-            bed_sequence = BedSequence(
-                "hg19",
-                "{cwd}/test.bed".format(
-                    cwd=os.path.dirname(os.path.abspath(__file__))
-                ),
-                batch_size
-            )
-            y = np.random.randint(
-                2,
-                size=(bed_sequence.samples_number, 1)
-            )
-            mixed_sequence = MixedSequence(
-                bed_sequence,
-                {
-                    "output1": y,
-                    "output2": bed_sequence
-                },
-                batch_size
-            )
-
-        Finally, we can proceed to use the obtained MixedSequence
-        to train our model:
-
-        .. code:: python
-
-            model.fit(
-                mixed_sequence,
-                steps_per_epoch=mixed_sequence.steps_per_epoch,
-                epochs=2,
-                verbose=0,
-                shuffle=True
-            )
 
 
         .. |travis| image:: https://travis-ci.org/LucaCappelletti94/keras_bed_sequence.png

keras_bed_sequence.egg-info/SOURCES.txt

@@ -10,4 +10,5 @@ keras_bed_sequence.egg-info/dependency_links.txt
 keras_bed_sequence.egg-info/requires.txt
 keras_bed_sequence.egg-info/top_level.txt
 keras_bed_sequence/utils/__init__.py
+keras_bed_sequence/utils/fast_to_categorical.py
 keras_bed_sequence/utils/nucleotides_to_numbers.py

keras_bed_sequence/__version__.py

@@ -1,2 +1,2 @@
 """Current version of package keras_bed_sequence"""
-__version__ = "1.1.2"
+__version__ = "1.1.3"