Committing the changes required to produce the results for the 2021 t…

…ransit paper

infer.py

@@ -3,25 +3,27 @@
 import datetime
 from pathlib import Path
 
-from ramjet.models.hades import Hades
+from ramjet.models.hades import Hades, FfiHades
+from ramjet.photometric_database.derived.tess_ffi_transit_databases import \
+    TessFfiStandardAndInjectedTransitAntiEclipsingBinaryDatabase
 from ramjet.photometric_database.derived.tess_two_minute_cadence_transit_databases import \
     TessTwoMinuteCadenceStandardAndInjectedTransitDatabase
 from ramjet.analysis.model_loader import get_latest_log_directory
 from ramjet.trial import infer
 
-log_name = get_latest_log_directory(logs_directory='logs')  # Uses the latest model in the log directory.
-# log_name = 'logs/baseline YYYY-MM-DD-hh-mm-ss'  # Specify the path to the model to use.
+# log_name = get_latest_log_directory(logs_directory='logs')  # Uses the latest model in the log directory.
+log_name = 'logs/FFI transit sai aeb FfiHades mag14 quick pos no neg cont from existing no random start 2020-12-19-16-10-27'  # Specify the path to the model to use.
 saved_log_directory = Path(f'{log_name}')
 datetime_string = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
 
 print('Setting up dataset...', flush=True)
-database = TessTwoMinuteCadenceStandardAndInjectedTransitDatabase()
+database = TessFfiStandardAndInjectedTransitAntiEclipsingBinaryDatabase()
 inference_dataset = database.generate_inference_dataset()
 
 print('Loading model...', flush=True)
-model = Hades(database.number_of_label_types)
+model = FfiHades()
 model.load_weights(str(saved_log_directory.joinpath('model.ckpt'))).expect_partial()
 
 print('Inferring...', flush=True)
 infer_results_path = saved_log_directory.joinpath(f'infer results {datetime_string}.csv')
-infer(model, inference_dataset, infer_results_path)
+infer(model, inference_dataset, infer_results_path, number_of_top_predictions_to_keep=5000)

ramjet/analysis/transit_fitter.py

@@ -350,7 +350,7 @@ def round_value_to_significant_figures(value):
 if __name__ == '__main__':
     print('Opening Bokeh application on http://localhost:5006/')
     # Start the server.
-    server = Server({'/': TransitFitter(tic_id=362043085).bokeh_application})
+    server = Server({'/': TransitFitter(tic_id=297678377).bokeh_application})
     server.start()
     # Start the specific application on the server.
     server.io_loop.add_callback(server.show, "/")

ramjet/models/hades.py

@@ -48,3 +48,48 @@ def call(self, inputs, training=False, mask=None):
         x = self.prediction_layer(x, training=training)
         outputs = self.reshape(x, training=training)
         return outputs
+
+
+class FfiHades(Model):
+    def __init__(self):
+        super().__init__()
+        self.block0 = LightCurveNetworkBlock(filters=8, kernel_size=3, pooling_size=2, batch_normalization=False,
+                                             dropout_rate=0)
+        self.block1 = LightCurveNetworkBlock(filters=8, kernel_size=3, pooling_size=2)
+        self.block2 = LightCurveNetworkBlock(filters=16, kernel_size=3, pooling_size=2)
+        self.block3 = LightCurveNetworkBlock(filters=32, kernel_size=3, pooling_size=2)
+        self.block4 = LightCurveNetworkBlock(filters=64, kernel_size=3, pooling_size=2)
+        self.block5 = LightCurveNetworkBlock(filters=128, kernel_size=3, pooling_size=2)
+        self.block6 = LightCurveNetworkBlock(filters=128, kernel_size=3, pooling_size=1)
+        self.block7 = LightCurveNetworkBlock(filters=128, kernel_size=3, pooling_size=1)
+        self.block8 = LightCurveNetworkBlock(filters=20, kernel_size=3, pooling_size=1, spatial=False)
+        self.block9 = LightCurveNetworkBlock(filters=20, kernel_size=7, pooling_size=1)
+        self.block10 = LightCurveNetworkBlock(filters=20, kernel_size=1, pooling_size=1, batch_normalization=False,
+                                              dropout_rate=0)
+        self.prediction_layer = Convolution1D(1, kernel_size=1, activation=sigmoid)
+        self.reshape = Reshape([1])
+
+    def call(self, inputs, training=False, mask=None):
+        """
+        The forward pass of the layer.
+
+        :param inputs: The input tensor.
+        :param training: A boolean specifying if the layer should be in training mode.
+        :param mask: A mask for the input tensor.
+        :return: The output tensor of the layer.
+        """
+        x = inputs
+        x = self.block0(x, training=training)
+        x = self.block1(x, training=training)
+        x = self.block2(x, training=training)
+        x = self.block3(x, training=training)
+        x = self.block4(x, training=training)
+        x = self.block5(x, training=training)
+        x = self.block6(x, training=training)
+        x = self.block7(x, training=training)
+        x = self.block8(x, training=training)
+        x = self.block9(x, training=training)
+        x = self.block10(x, training=training)
+        x = self.prediction_layer(x, training=training)
+        outputs = self.reshape(x, training=training)
+        return outputs

ramjet/photometric_database/derived/tess_ffi_eclipsing_binary_lightcurve_collection.py

@@ -1,7 +1,9 @@
 """
 Code representing the collection of TESS two minute cadence lightcurves containing eclipsing binaries.
 """
-from typing import Union, List
+import pandas as pd
+from pathlib import Path
+from typing import Union, List, Iterable
 
 from peewee import Select
 
@@ -56,3 +58,15 @@ def get_sql_query(self) -> Select:
             TessEclipsingBinaryMetadata.tic_id.not_in(transit_tic_id_query))
         query = query.where(TessFfiLightcurveMetadata.tic_id.in_(eclipsing_binary_tic_id_query))
         return query
+
+
+class TessFfiQuickTransitNegativeLightcurveCollection(TessFfiLightcurveCollection):
+    def __init__(self, dataset_splits: Union[List[int], None] = None,
+                 magnitude_range: (Union[float, None], Union[float, None]) = (None, None)):
+        super().__init__(dataset_splits=dataset_splits, magnitude_range=magnitude_range)
+        self.label = 0
+
+    def get_paths(self) -> Iterable[Path]:
+        data_frame = pd.read_csv('quick_negative_paths.csv')
+        paths = list(map(Path, data_frame['Lightcurve path'].values))
+        return paths

ramjet/photometric_database/derived/tess_ffi_lightcurve_collection.py

@@ -34,8 +34,8 @@ def get_sql_query(self) -> Select:
         :return: The SQL query.
         """
         query = TessFfiLightcurveMetadata().select()
-        query = self.order_by_dataset_split_with_random_start(query, TessFfiLightcurveMetadata.dataset_split,
-                                                              self.dataset_splits)
+        # query = self.order_by_dataset_split_with_random_start(query, TessFfiLightcurveMetadata.dataset_split,
+        #                                                       self.dataset_splits)
         if self.magnitude_range[0] is not None and self.magnitude_range[1] is not None:
             query = query.where(TessFfiLightcurveMetadata.magnitude.between(*self.magnitude_range))
         elif self.magnitude_range[0] is not None:

ramjet/photometric_database/derived/tess_ffi_transit_databases.py

@@ -1,5 +1,5 @@
 from ramjet.photometric_database.derived.tess_ffi_eclipsing_binary_lightcurve_collection import \
-    TessFfiAntiEclipsingBinaryForTransitLightcurveCollection
+    TessFfiAntiEclipsingBinaryForTransitLightcurveCollection, TessFfiQuickTransitNegativeLightcurveCollection
 from ramjet.photometric_database.derived.tess_ffi_lightcurve_collection import TessFfiLightcurveCollection
 from ramjet.photometric_database.derived.tess_ffi_transit_lightcurve_collections import \
     TessFfiConfirmedTransitLightcurveCollection, TessFfiNonTransitLightcurveCollection
@@ -15,11 +15,11 @@ def __init__(self):
         super().__init__()
         self.batch_size = 1000
         self.time_steps_per_example = 1000
-        self.shuffle_buffer_size = 100000
+        self.shuffle_buffer_size = 10000
         self.out_of_bounds_injection_handling = OutOfBoundsInjectionHandlingMethod.RANDOM_INJECTION_LOCATION
 
 
-magnitude_range = (0, 11)
+magnitude_range = (0, 14)
 
 
 class TessFfiStandardTransitDatabase(TessFfiDatabase):
@@ -106,23 +106,32 @@ class TessFfiStandardAndInjectedTransitAntiEclipsingBinaryDatabase(TessFfiDataba
     """
     def __init__(self):
         super().__init__()
+        self.shuffle_buffer_size = 10000
+        self.number_of_parallel_processes_per_map = 6
         self.training_standard_lightcurve_collections = [
             TessFfiConfirmedTransitLightcurveCollection(dataset_splits=list(range(8)), magnitude_range=magnitude_range),
             TessFfiNonTransitLightcurveCollection(dataset_splits=list(range(8)), magnitude_range=magnitude_range),
             TessFfiAntiEclipsingBinaryForTransitLightcurveCollection(dataset_splits=list(range(8)),
-                                                                     magnitude_range=magnitude_range)
+                                                                     magnitude_range=magnitude_range),
+            # TessFfiQuickTransitNegativeLightcurveCollection(dataset_splits=list(range(8)),
+            #                                                 magnitude_range=magnitude_range)
         ]
         self.training_injectee_lightcurve_collection = TessFfiNonTransitLightcurveCollection(
             dataset_splits=list(range(8)), magnitude_range=magnitude_range)
         self.training_injectable_lightcurve_collections = [
             TessFfiConfirmedTransitLightcurveCollection(dataset_splits=list(range(8)), magnitude_range=magnitude_range),
             TessFfiNonTransitLightcurveCollection(dataset_splits=list(range(8)), magnitude_range=magnitude_range),
             TessFfiAntiEclipsingBinaryForTransitLightcurveCollection(dataset_splits=list(range(8)),
-                                                                     magnitude_range=magnitude_range)
+                                                                     magnitude_range=magnitude_range),
+            # TessFfiQuickTransitNegativeLightcurveCollection(dataset_splits=list(range(8)),
+            #                                                 magnitude_range=magnitude_range)
         ]
         self.validation_standard_lightcurve_collections = [
             TessFfiConfirmedTransitLightcurveCollection(dataset_splits=[8], magnitude_range=magnitude_range),
             TessFfiNonTransitLightcurveCollection(dataset_splits=[8], magnitude_range=magnitude_range),
             TessFfiAntiEclipsingBinaryForTransitLightcurveCollection(dataset_splits=list(range(8)),
-                                                                     magnitude_range=magnitude_range)
+                                                                     magnitude_range=magnitude_range),
+            # TessFfiQuickTransitNegativeLightcurveCollection(dataset_splits=list(range(8)),
+            #                                                 magnitude_range=magnitude_range)
         ]
+        self.inference_lightcurve_collections = [TessFfiLightcurveCollection(magnitude_range=magnitude_range)]

train.py

@@ -5,7 +5,10 @@
 from tensorflow.python.keras import callbacks
 from tensorflow.python.keras.losses import BinaryCrossentropy
 
-from ramjet.models.hades import Hades
+from ramjet.basic_models import SimplePoolingLightcurveCnn2, FfiSimplePoolingLightcurveCnn2
+from ramjet.models.hades import Hades, FfiHades
+from ramjet.photometric_database.derived.tess_ffi_transit_databases import \
+    TessFfiStandardAndInjectedTransitAntiEclipsingBinaryDatabase
 from ramjet.photometric_database.derived.tess_two_minute_cadence_transit_databases import \
     TessTwoMinuteCadenceStandardAndInjectedTransitDatabase
 
@@ -14,9 +17,9 @@ def train():
     """Runs the training."""
     print('Starting training process...', flush=True)
     # Basic training settings.
-    trial_name = f'baseline'  # Add any desired run name details to this string.
-    database = TessTwoMinuteCadenceStandardAndInjectedTransitDatabase()
-    model = Hades(database.number_of_label_types)
+    trial_name = f'FFI transit sai aeb FfiHades mag14 quick pos no neg cont from existing no random start'  # Add any desired run name details to this string.
+    model = FfiHades()
+    database = TessFfiStandardAndInjectedTransitAntiEclipsingBinaryDatabase()
     # database.batch_size = 100  # Reducing the batch size may help if you are running out of memory.
     epochs_to_run = 1000
     logs_directory = 'logs'
@@ -33,14 +36,15 @@ def train():
     training_dataset, validation_dataset = database.generate_datasets()
     optimizer = tf.optimizers.Adam(learning_rate=1e-4)
     loss_metric = BinaryCrossentropy(name='Loss')
-    metrics = [tf.keras.metrics.AUC(num_thresholds=20, name='Area_under_ROC_curve', multi_label=True),
+    metrics = [tf.keras.metrics.AUC(num_thresholds=20, name='Area_under_ROC_curve'),
                tf.metrics.SpecificityAtSensitivity(0.9, name='Specificity_at_90_percent_sensitivity'),
                tf.metrics.SensitivityAtSpecificity(0.9, name='Sensitivity_at_90_percent_specificity'),
                tf.metrics.BinaryAccuracy(name='Accuracy'), tf.metrics.Precision(name='Precision'),
                tf.metrics.Recall(name='Recall')]
 
     # Compile and train model.
     model.compile(optimizer=optimizer, loss=loss_metric, metrics=metrics)
+    model.load_weights('/att/gpfsfs/briskfs01/ppl/golmsche/ramjet/logs/FFI transit sai aeb FfiHades mag13 quick pos no neg cont from existing no random start 2020-10-08-17-11-05/model.ckpt')
     try:
         model.fit(training_dataset, epochs=epochs_to_run, validation_data=validation_dataset,
                   callbacks=[tensorboard_callback, model_checkpoint_callback], steps_per_epoch=5000,